1
|
Bialves TS, Bastos LL, Parra JAA, Moysés MN, Marques E, de Castro Pimenta AM, Quintela FM, Mariano DCB, Carvalho FC, de Melo-Minardi RC, Boyle RT. Interaction of DisBa01 peptide from Bothrops alternatus venom with BRAF melanoma receptors: Modeling and molecular docking. Int J Biol Macromol 2024; 274:133283. [PMID: 38909731 DOI: 10.1016/j.ijbiomac.2024.133283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 06/12/2024] [Accepted: 06/18/2024] [Indexed: 06/25/2024]
Abstract
Metastatic melanoma is highly aggressive and challenging, often leading to a grim prognosis. Its progression is swift, especially when mutations like BRAFV600E continuously activate pathways vital for cell growth and survival. Although several treatments target this mutation, resistance typically emerges over time. In recent decades, research has underscored the potential of snake venoms and peptides as bioactive substances for innovative drugs, including anti-coagulants, anti-microbial, and anti-cancer agents. Leveraging this knowledge, we propose employing a bioinformatics simulation approach to: a) Predict how well a peptide (DisBa01) from Bothrops alternatus snake venom binds to the melanoma receptor BRAFV600E via Molecular Docking. b) Identify the specific peptide binding sites on receptors and analyze their proximity to active receptor sites. c) Evaluate the behavior of resulting complexes through molecular dynamics simulations. d) Assess whether this peptide qualifies as a candidate for anti-melanoma therapy. Our findings reveal that DisBa01 enhances stability in the BRAFV600E melanoma receptor structure by binding to its RGD motif, an interaction absent in the BRAF WT model. Consequently, both docking and molecular dynamics simulations suggest that DisBa01 shows promise as a BRAFV600E inhibitor.
Collapse
Affiliation(s)
- Tatiane Senna Bialves
- Graduate Program in Physiological Sciences (PPGCF), Federal University of Rio Grande - FURG, Av. Italy, s/n - km 8 - Carreiros, Rio Grande, Rio Grande do Sul, Brazil.
| | - Luana Luiza Bastos
- Laboratory of Bioinformatics and Systems, Institute of Exact Sciences, Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - John Alexanders Amaya Parra
- Graduate Program in Biochemistry and Immunology, Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Maurício Nogueira Moysés
- Graduate Program in Biochemistry and Immunology, Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Edleusa Marques
- Graduate Program in Biochemistry and Immunology, Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Adriano Monteiro de Castro Pimenta
- Graduate Program in Biochemistry and Immunology, Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Fernando Marques Quintela
- Instituto Nacional de Pesquisas do Pantanal- Museu Paraense Emílio Goeldi, Av. Magalhães Barata, 376, Belém, Pará, Brazil
| | - Diego César Batista Mariano
- Laboratory of Bioinformatics and Systems, Institute of Exact Sciences, Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Frederico Chaves Carvalho
- Laboratory of Bioinformatics and Systems, Institute of Exact Sciences, Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Raquel C de Melo-Minardi
- Laboratory of Bioinformatics and Systems, Institute of Exact Sciences, Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Robert Tew Boyle
- Graduate Program in Physiological Sciences (PPGCF), Federal University of Rio Grande - FURG, Av. Italy, s/n - km 8 - Carreiros, Rio Grande, Rio Grande do Sul, Brazil
| |
Collapse
|
2
|
Reddy DJ, Guntuku G, Palla MS. Advancements in nanobody generation: Integrating conventional, in silico, and machine learning approaches. Biotechnol Bioeng 2024. [PMID: 39054738 DOI: 10.1002/bit.28816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Revised: 07/08/2024] [Accepted: 07/13/2024] [Indexed: 07/27/2024]
Abstract
Nanobodies, derived from camelids and sharks, offer compact, single-variable heavy-chain antibodies with diverse biomedical potential. This review explores their generation methods, including display techniques on phages, yeast, or bacteria, and computational methodologies. Integrating experimental and computational approaches enhances understanding of nanobody structure and function. Future trends involve leveraging next-generation sequencing, machine learning, and artificial intelligence for efficient candidate selection and predictive modeling. The convergence of traditional and computational methods promises revolutionary advancements in precision biomedical applications such as targeted drug delivery and diagnostics. Embracing these technologies accelerates nanobody development, driving transformative breakthroughs in biomedicine and paving the way for precision medicine and biomedical innovation.
Collapse
Affiliation(s)
- D Jagadeeswara Reddy
- Pharmaceutical Biotechnology Division, A.U. College of Pharmaceutical Sciences, Andhra University, Visakhapatnam, India
| | - Girijasankar Guntuku
- Pharmaceutical Biotechnology Division, A.U. College of Pharmaceutical Sciences, Andhra University, Visakhapatnam, India
| | - Mary Sulakshana Palla
- GITAM School of Pharmacy, GITAM Deemed to be University, Rushikonda, Visakhapatnam, India
| |
Collapse
|
3
|
Xiao Y, Yuan Y, Liu Y, Lin Z, Zheng G, Zhou D, Lv D. Targeted Protein Degradation: Current and Emerging Approaches for E3 Ligase Deconvolution. J Med Chem 2024; 67:11580-11596. [PMID: 38981094 DOI: 10.1021/acs.jmedchem.4c00723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
Targeted protein degradation (TPD), including the use of proteolysis-targeting chimeras (PROTACs) and molecular glue degraders (MGDs) to degrade proteins, is an emerging strategy to develop novel therapies for cancer and beyond. PROTACs or MGDs function by inducing the proximity between an E3 ligase and a protein of interest (POI), leading to ubiquitination and consequent proteasomal degradation of the POI. Notably, one major issue in TPD is the lack of ligandable E3 ligases, as current studies predominantly use CUL4CRBN and CUL2VHL. The TPD community is seeking to expand the landscape of ligandable E3 ligases, but most discoveries rely on phenotypic screens or serendipity, necessitating systematic target deconvolution. Here, we examine and discuss both current and emerging E3 ligase deconvolution approaches for degraders discovered from phenotypic screens or monovalent glue chemistry campaigns, highlighting future prospects for identifying more ligandable E3 ligases.
Collapse
Affiliation(s)
- Yufeng Xiao
- Department of Medicinal Chemistry, College of Pharmacy, University of Florida, 1333 Center Drive, Gainesville, Florida 32610, United States
| | - Yaxia Yuan
- Department of Biochemistry and Structural Biology and Center for Innovative Drug Discovery, School of Medicine, University of Texas Health San Antonio, 7703 Floyd Curl Drive, San Antonio, Texas 78229, United States
- Mays Cancer Center, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, Texas 78229, United States
| | - Yi Liu
- Department of Medicinal Chemistry, College of Pharmacy, University of Florida, 1333 Center Drive, Gainesville, Florida 32610, United States
| | - Zongtao Lin
- Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, St. Louis, Missouri 63110, United States
| | - Guangrong Zheng
- Department of Medicinal Chemistry, College of Pharmacy, University of Florida, 1333 Center Drive, Gainesville, Florida 32610, United States
| | - Daohong Zhou
- Department of Biochemistry and Structural Biology and Center for Innovative Drug Discovery, School of Medicine, University of Texas Health San Antonio, 7703 Floyd Curl Drive, San Antonio, Texas 78229, United States
- Mays Cancer Center, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, Texas 78229, United States
| | - Dongwen Lv
- Department of Biochemistry and Structural Biology and Center for Innovative Drug Discovery, School of Medicine, University of Texas Health San Antonio, 7703 Floyd Curl Drive, San Antonio, Texas 78229, United States
- Mays Cancer Center, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, Texas 78229, United States
| |
Collapse
|
4
|
Wossnig L, Furtmann N, Buchanan A, Kumar S, Greiff V. Best practices for machine learning in antibody discovery and development. Drug Discov Today 2024; 29:104025. [PMID: 38762089 DOI: 10.1016/j.drudis.2024.104025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 04/25/2024] [Accepted: 05/13/2024] [Indexed: 05/20/2024]
Abstract
In the past 40 years, therapeutic antibody discovery and development have advanced considerably, with machine learning (ML) offering a promising way to speed up the process by reducing costs and the number of experiments required. Recent progress in ML-guided antibody design and development (D&D) has been hindered by the diversity of data sets and evaluation methods, which makes it difficult to conduct comparisons and assess utility. Establishing standards and guidelines will be crucial for the wider adoption of ML and the advancement of the field. This perspective critically reviews current practices, highlights common pitfalls and proposes method development and evaluation guidelines for various ML-based techniques in therapeutic antibody D&D. Addressing challenges across the ML process, best practices are recommended for each stage to enhance reproducibility and progress.
Collapse
Affiliation(s)
- Leonard Wossnig
- LabGenius Ltd, The Biscuit Factory, 100 Drummond Road, London SE16 4DG, UK; Department of Computer Science, University College London, 66-72 Gower St, London WC1E 6EA, UK.
| | - Norbert Furtmann
- R&D Large Molecules Research Platform, Sanofi Deutschland GmbH, Industriepark Höchst, Frankfurt Am Main, Germany
| | - Andrew Buchanan
- Biologics Engineering, R&D, AstraZeneca, Cambridge CB2 0AA, UK
| | - Sandeep Kumar
- Computational Protein Design and Modeling Group, Computational Science, Moderna Therapeutics, 200 Technology Square, Cambridge, MA 02139, USA
| | - Victor Greiff
- Department of Immunology and Oslo University Hospital, University of Oslo, Oslo, Norway
| |
Collapse
|
5
|
Kousaka S, Ishikawa T. Quantum Chemistry-Based Protein-Protein Docking without Empirical Parameters. J Chem Theory Comput 2024; 20:5164-5175. [PMID: 38845143 DOI: 10.1021/acs.jctc.4c00531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
This study developed a novel protein-protein docking approach based on quantum chemistry. To judge the appropriateness of complex structures, we introduced two criterion values, EV1 and EV2, computed using the fragment molecular orbital method without any empirical parameters. These criterion values enable us to search complex structures in which patterns of the electrostatic potential of the two proteins are optimally aligned at their interface. The performance of our method was validated using 53 complexes in a benchmark set provided for protein-protein docking. When employing bound state structures, docking success rates reached 64% for EV1 and 76% for EV2. On the other hand, when employing unbound state structures, docking success rates reached 13% for EV1 and 17% for EV2.
Collapse
Affiliation(s)
- Sumire Kousaka
- Department of Chemistry, Biotechnology, and Chemical Engineering, Graduate School of Science and Engineering, Kagoshima University, 1-21-40 Korimoto, Kagoshima 890-0065, Japan
| | - Takeshi Ishikawa
- Department of Chemistry, Biotechnology, and Chemical Engineering, Graduate School of Science and Engineering, Kagoshima University, 1-21-40 Korimoto, Kagoshima 890-0065, Japan
| |
Collapse
|
6
|
Zhao N, Wu T, Wang W, Zhang L, Gong X. Review and Comparative Analysis of Methods and Advancements in Predicting Protein Complex Structure. Interdiscip Sci 2024; 16:261-288. [PMID: 38955920 DOI: 10.1007/s12539-024-00626-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 02/29/2024] [Accepted: 03/01/2024] [Indexed: 07/04/2024]
Abstract
Protein complexes perform diverse biological functions, and obtaining their three-dimensional structure is critical to understanding and grasping their functions. In many cases, it's not just two proteins interacting to form a dimer; instead, multiple proteins interact to form a multimer. Experimentally resolving protein complex structures can be quite challenging. Recently, there have been efforts and methods that build upon prior predictions of dimer structures to attempt to predict multimer structures. However, in comparison to monomeric protein structure prediction, the accuracy of protein complex structure prediction remains relatively low. This paper provides an overview of recent advancements in efficient computational models for predicting protein complex structures. We introduce protein-protein docking methods in detail and summarize their main ideas, applicable modes, and related information. To enhance prediction accuracy, other critical protein-related information is also integrated, such as predicting interchain residue contact, utilizing experimental data like cryo-EM experiments, and considering protein interactions and non-interactions. In addition, we comprehensively review computational approaches for end-to-end prediction of protein complex structures based on artificial intelligence (AI) technology and describe commonly used datasets and representative evaluation metrics in protein complexes. Finally, we analyze the formidable challenges faced in current protein complex structure prediction tasks, including the structure prediction of heteromeric complex, disordered regions in complex, antibody-antigen complex, and RNA-related complex, as well as the evaluation metrics for complex assessment. We hope that this work will provide comprehensive knowledge of complex structure predictions to contribute to future advanced predictions.
Collapse
Affiliation(s)
- Nan Zhao
- Institute for Mathematical Sciences, Renmin University of China, Beijing, 100872, China
- School of Mathematics, Renmin University of China, Beijing, 100872, China
| | - Tong Wu
- Institute for Mathematical Sciences, Renmin University of China, Beijing, 100872, China
- School of Mathematics, Renmin University of China, Beijing, 100872, China
| | - Wenda Wang
- Institute for Mathematical Sciences, Renmin University of China, Beijing, 100872, China
- School of Mathematics, Renmin University of China, Beijing, 100872, China
| | - Lunchuan Zhang
- School of Mathematics, Renmin University of China, Beijing, 100872, China.
| | - Xinqi Gong
- Institute for Mathematical Sciences, Renmin University of China, Beijing, 100872, China.
- School of Mathematics, Renmin University of China, Beijing, 100872, China.
- Beijing Academy of Artificial Intelligence, Beijing, 100084, China.
| |
Collapse
|
7
|
Chen X, Liu J, Park N, Cheng J. A Survey of Deep Learning Methods for Estimating the Accuracy of Protein Quaternary Structure Models. Biomolecules 2024; 14:574. [PMID: 38785981 PMCID: PMC11117562 DOI: 10.3390/biom14050574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 04/07/2024] [Accepted: 05/09/2024] [Indexed: 05/25/2024] Open
Abstract
The quality prediction of quaternary structure models of a protein complex, in the absence of its true structure, is known as the Estimation of Model Accuracy (EMA). EMA is useful for ranking predicted protein complex structures and using them appropriately in biomedical research, such as protein-protein interaction studies, protein design, and drug discovery. With the advent of more accurate protein complex (multimer) prediction tools, such as AlphaFold2-Multimer and ESMFold, the estimation of the accuracy of protein complex structures has attracted increasing attention. Many deep learning methods have been developed to tackle this problem; however, there is a noticeable absence of a comprehensive overview of these methods to facilitate future development. Addressing this gap, we present a review of deep learning EMA methods for protein complex structures developed in the past several years, analyzing their methodologies, data and feature construction. We also provide a prospective summary of some potential new developments for further improving the accuracy of the EMA methods.
Collapse
Affiliation(s)
- Xiao Chen
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
| | - Jian Liu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
- NextGen Precision Health Institute, University of Missouri, Columbia, MO 65211, USA
| | - Nolan Park
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
- NextGen Precision Health Institute, University of Missouri, Columbia, MO 65211, USA
| |
Collapse
|
8
|
Liu JX, Zhang X, Huang YQ, Hao GF, Yang GF. Multi-level bioinformatics resources support drug target discovery of protein-protein interactions. Drug Discov Today 2024; 29:103979. [PMID: 38608830 DOI: 10.1016/j.drudis.2024.103979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 03/14/2024] [Accepted: 04/05/2024] [Indexed: 04/14/2024]
Abstract
Drug discovery often begins with a new target. Protein-protein interactions (PPIs) are crucial to multitudinous cellular processes and offer a promising avenue for drug-target discovery. PPIs are characterized by multi-level complexity: at the protein level, interaction networks can be used to identify potential targets, whereas at the residue level, the details of the interactions of individual PPIs can be used to examine a target's druggability. Much great progress has been made in target discovery through multi-level PPI-related computational approaches, but these resources have not been fully discussed. Here, we systematically survey bioinformatics tools for identifying and assessing potential drug targets, examining their characteristics, limitations and applications. This work will aid the integration of the broader protein-to-network context with the analysis of detailed binding mechanisms to support the discovery of drug targets.
Collapse
Affiliation(s)
- Jia-Xin Liu
- National Key Laboratory of Green Pesticide, Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, PR China
| | - Xiao Zhang
- State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
| | - Yuan-Qin Huang
- State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
| | - Ge-Fei Hao
- National Key Laboratory of Green Pesticide, Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, PR China; State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals, Guizhou University, Guiyang 550025, PR China.
| | - Guang-Fu Yang
- National Key Laboratory of Green Pesticide, Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, PR China.
| |
Collapse
|
9
|
Shang W, Hu X, Lin X, Li S, Xiong S, Huang B, Wang X. Iterative In Silico Screening for Optimizing Stable Conformation of Anti-SARS-CoV-2 Nanobodies. Pharmaceuticals (Basel) 2024; 17:424. [PMID: 38675386 PMCID: PMC11054880 DOI: 10.3390/ph17040424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 03/20/2024] [Accepted: 03/23/2024] [Indexed: 04/28/2024] Open
Abstract
Nanobodies (Nbs or VHHs) are single-domain antibodies (sdAbs) derived from camelid heavy-chain antibodies. Nbs have special and unique characteristics, such as small size, good tissue penetration, and cost-effective production, making Nbs a good candidate for the diagnosis and treatment of viruses and other pathologies. Identifying effective Nbs against COVID-19 would help us control this dangerous virus or other unknown variants in the future. Herein, we introduce an in silico screening strategy for optimizing stable conformation of anti-SARS-CoV-2 Nbs. Firstly, various complexes containing nanobodies were downloaded from the RCSB database, which were identified from immunized llamas. The primary docking between Nbs and the SARS-CoV-2 spike protein receptor-binding domain was performed through the ClusPro program, with the manual screening leaving the reasonable conformation to the next step. Then, the binding distances of atoms between the antigen-antibody interfaces were measured through the NeighborSearch algorithm. Finally, filtered nanobodies were acquired according to HADDOCK scores through HADDOCK docking the COVID-19 spike protein with nanobodies under restrictions of calculated molecular distance between active residues and antigenic epitopes less than 4.5 Å. In this way, those nanobodies with more reasonable conformation and stronger neutralizing efficacy were acquired. To validate the efficacy ranking of the nanobodies we obtained, we calculated the binding affinities (∆G) and dissociation constants (Kd) of all screened nanobodies using the PRODIGY web tool and predicted the stability changes induced by all possible point mutations in nanobodies using the MAESTROWeb server. Furthermore, we examined the performance of the relationship between nanobodies' ranking and their number of mutation-sensitive sites (Spearman correlation > 0.68); the results revealed a robust correlation, indicating that the superior nanobodies identified through our screening process exhibited fewer mutation hotspots and higher stability. This correlation analysis demonstrates the validity of our screening criteria, underscoring the suitability of these nanobodies for future development and practical implementation. In conclusion, this three-step screening strategy iteratively in silico greatly improved the accuracy of screening desired nanobodies compared to using only ClusPro docking or default HADDOCK docking settings. It provides new ideas for the screening of novel antibodies and computer-aided screening methods.
Collapse
Affiliation(s)
| | | | | | | | | | - Bingding Huang
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China; (W.S.); (X.H.); (X.L.); (S.L.); (S.X.)
| | - Xin Wang
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China; (W.S.); (X.H.); (X.L.); (S.L.); (S.X.)
| |
Collapse
|
10
|
Christoffer C, Harini K, Archit G, Kihara D. Assembly of Protein Complexes in and on the Membrane with Predicted Spatial Arrangement Constraints. J Mol Biol 2024; 436:168486. [PMID: 38336197 PMCID: PMC10942765 DOI: 10.1016/j.jmb.2024.168486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 01/17/2024] [Accepted: 02/05/2024] [Indexed: 02/12/2024]
Abstract
Membrane proteins play crucial roles in various cellular processes, and their interactions with other proteins in and on the membrane are essential for their proper functioning. While an increasing number of structures of more membrane proteins are being determined, the available structure data is still sparse. To gain insights into the mechanisms of membrane protein complexes, computational docking methods are necessary due to the challenge of experimental determination. Here, we introduce Mem-LZerD, a rigid-body membrane docking algorithm designed to take advantage of modern membrane modeling and protein docking techniques to facilitate the docking of membrane protein complexes. Mem-LZerD is based on the LZerD protein docking algorithm, which has been constantly among the top servers in many rounds of CAPRI protein docking assessment. By employing a combination of geometric hashing, newly constrained by the predicted membrane height and tilt angle, and model scoring accounting for the energy of membrane insertion, we demonstrate the capability of Mem-LZerD to model diverse membrane protein-protein complexes. Mem-LZerD successfully performed unbound docking on 13 of 21 (61.9%) transmembrane complexes in an established benchmark, more than shown by previous approaches. It was additionally tested on new datasets of 44 transmembrane complexes and 92 peripheral membrane protein complexes, of which it successfully modeled 35 (79.5%) and 15 (16.3%) complexes respectively. When non-blind orientations of peripheral targets were included, the number of successes increased to 54 (58.7%). We further demonstrate that Mem-LZerD produces complex models which are suitable for molecular dynamics simulation. Mem-LZerD is made available at https://lzerd.kiharalab.org.
Collapse
Affiliation(s)
- Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Kannan Harini
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India; Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
| | - Gupta Archit
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA; Department of Genetic Engineering, SRM Institute of Science and Technology, Kattankulathur 603203, India
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA; Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA; Purdue University Center for Cancer Research, Purdue University, West Lafayette, IN 47907, USA.
| |
Collapse
|
11
|
Tse LH, Cheung ST, Lee S, Wong YH. Real-Time Determination of Intracellular cAMP Reveals Functional Coupling of G s Protein to the Melatonin MT 1 Receptor. Int J Mol Sci 2024; 25:2919. [PMID: 38474167 DOI: 10.3390/ijms25052919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 02/23/2024] [Accepted: 02/28/2024] [Indexed: 03/14/2024] Open
Abstract
Melatonin is a neuroendocrine hormone that regulates the circadian rhythm and many other physiological processes. Its functions are primarily exerted through two subtypes of human melatonin receptors, termed melatonin type-1 (MT1) and type-2 (MT2) receptors. Both MT1 and MT2 receptors are generally classified as Gi-coupled receptors owing to their well-recognized ability to inhibit cAMP accumulation in cells. However, it remains an enigma as to why melatonin stimulates cAMP production in a number of cell types that express the MT1 receptor. To address if MT1 can dually couple to Gs and Gi proteins, we employed a highly sensitive luminescent biosensor (GloSensorTM) to monitor the real-time changes in the intracellular cAMP level in intact live HEK293 cells that express MT1 and/or MT2. Our results demonstrate that the activation of MT1, but not MT2, leads to a robust enhancement on the forskolin-stimulated cAMP formation. In contrast, the activation of either MT1 or MT2 inhibited cAMP synthesis driven by the activation of the Gs-coupled β2-adrenergic receptor, which is consistent with a typical Gi-mediated response. The co-expression of MT1 with Gs enabled melatonin itself to stimulate cAMP production, indicating a productive coupling between MT1 and Gs. The possible existence of a MT1-Gs complex was supported through molecular modeling as the predicted complex exhibited structural and thermodynamic characteristics that are comparable to that of MT1-Gi. Taken together, our data reveal that MT1, but not MT2, can dually couple to Gs and Gi proteins, thereby enabling the bi-directional regulation of adenylyl cyclase to differentially modulate cAMP levels in cells that express different complements of MT1, MT2, and G proteins.
Collapse
Affiliation(s)
- Lap Hang Tse
- Division of Life Science and the Biotechnology Research Institute, Hong Kong University of Science and Technology, Hong Kong, China
| | - Suet Ting Cheung
- Division of Life Science and the Biotechnology Research Institute, Hong Kong University of Science and Technology, Hong Kong, China
| | - Seayoung Lee
- Division of Life Science and the Biotechnology Research Institute, Hong Kong University of Science and Technology, Hong Kong, China
| | - Yung Hou Wong
- Division of Life Science and the Biotechnology Research Institute, Hong Kong University of Science and Technology, Hong Kong, China
- State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, Hong Kong University of Science and Technology, Hong Kong, China
- Hong Kong Center for Neurodegenerative Diseases, 17 Science Park West Avenue, Hong Kong Science Park, Shatin, Hong Kong, China
| |
Collapse
|
12
|
Li H, Sun X, Cui W, Xu M, Dong J, Ekundayo BE, Ni D, Rao Z, Guo L, Stahlberg H, Yuan S, Vogel H. Computational drug development for membrane protein targets. Nat Biotechnol 2024; 42:229-242. [PMID: 38361054 DOI: 10.1038/s41587-023-01987-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 09/13/2023] [Indexed: 02/17/2024]
Abstract
The application of computational biology in drug development for membrane protein targets has experienced a boost from recent developments in deep learning-driven structure prediction, increased speed and resolution of structure elucidation, machine learning structure-based design and the evaluation of big data. Recent protein structure predictions based on machine learning tools have delivered surprisingly reliable results for water-soluble and membrane proteins but have limitations for development of drugs that target membrane proteins. Structural transitions of membrane proteins have a central role during transmembrane signaling and are often influenced by therapeutic compounds. Resolving the structural and functional basis of dynamic transmembrane signaling networks, especially within the native membrane or cellular environment, remains a central challenge for drug development. Tackling this challenge will require an interplay between experimental and computational tools, such as super-resolution optical microscopy for quantification of the molecular interactions of cellular signaling networks and their modulation by potential drugs, cryo-electron microscopy for determination of the structural transitions of proteins in native cell membranes and entire cells, and computational tools for data analysis and prediction of the structure and function of cellular signaling networks, as well as generation of promising drug candidates.
Collapse
Affiliation(s)
- Haijian Li
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
| | - Xiaolin Sun
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
| | - Wenqiang Cui
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Marc Xu
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Junlin Dong
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Babatunde Edukpe Ekundayo
- Laboratory of Biological Electron Microscopy, IPHYS, SB, EPFL and Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Dongchun Ni
- Laboratory of Biological Electron Microscopy, IPHYS, SB, EPFL and Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Zhili Rao
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
| | - Liwei Guo
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
| | - Henning Stahlberg
- Laboratory of Biological Electron Microscopy, IPHYS, SB, EPFL and Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.
| | - Shuguang Yuan
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China.
| | - Horst Vogel
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China.
- Institut des Sciences et Ingénierie Chimiques (ISIC), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| |
Collapse
|
13
|
Zhao N, Han B, Zhao C, Xu J, Gong X. ABAG-docking benchmark: a non-redundant structure benchmark dataset for antibody-antigen computational docking. Brief Bioinform 2024; 25:bbae048. [PMID: 38385879 PMCID: PMC10883643 DOI: 10.1093/bib/bbae048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 01/05/2024] [Accepted: 01/15/2024] [Indexed: 02/23/2024] Open
Abstract
Accurate prediction of antibody-antigen complex structures is pivotal in drug discovery, vaccine design and disease treatment and can facilitate the development of more effective therapies and diagnostics. In this work, we first review the antibody-antigen docking (ABAG-docking) datasets. Then, we present the creation and characterization of a comprehensive benchmark dataset of antibody-antigen complexes. We categorize the dataset based on docking difficulty, interface properties and structural characteristics, to provide a diverse set of cases for rigorous evaluation. Compared with Docking Benchmark 5.5, we have added 112 cases, including 14 single-domain antibody (sdAb) cases and 98 monoclonal antibody (mAb) cases, and also increased the proportion of Difficult cases. Our dataset contains diverse cases, including human/humanized antibodies, sdAbs, rodent antibodies and other types, opening the door to better algorithm development. Furthermore, we provide details on the process of building the benchmark dataset and introduce a pipeline for periodic updates to keep it up to date. We also utilize multiple complex prediction methods including ZDOCK, ClusPro, HDOCK and AlphaFold-Multimer for testing and analyzing this dataset. This benchmark serves as a valuable resource for evaluating and advancing docking computational methods in the analysis of antibody-antigen interaction, enabling researchers to develop more accurate and effective tools for predicting and designing antibody-antigen complexes. The non-redundant ABAG-docking structure benchmark dataset is available at https://github.com/Zhaonan99/Antibody-antigen-complex-structure-benchmark-dataset.
Collapse
Affiliation(s)
- Nan Zhao
- Institute for Mathematical Sciences, School of Mathematics, Renmin University of China, Beijing, China
| | - Bingqing Han
- Institute for Mathematical Sciences, School of Mathematics, Renmin University of China, Beijing, China
| | - Cuicui Zhao
- Institute for Mathematical Sciences, School of Mathematics, Renmin University of China, Beijing, China
| | - Jinbo Xu
- MoleculeMind Ltd., Beijing, China
| | - Xinqi Gong
- Institute for Mathematical Sciences, School of Mathematics, Renmin University of China, Beijing, China
- Beijing Academy of Artificial Intelligence, Beijing, China
| |
Collapse
|
14
|
Zhang Y, Wang X, Zhang Z, Huang Y, Kihara D. Assessment of Protein-Protein Docking Models Using Deep Learning. Methods Mol Biol 2024; 2780:149-162. [PMID: 38987469 DOI: 10.1007/978-1-0716-3985-6_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2024]
Abstract
Protein-protein interactions are involved in almost all processes in a living cell and determine the biological functions of proteins. To obtain mechanistic understandings of protein-protein interactions, the tertiary structures of protein complexes have been determined by biophysical experimental methods, such as X-ray crystallography and cryogenic electron microscopy. However, as experimental methods are costly in resources, many computational methods have been developed that model protein complex structures. One of the difficulties in computational protein complex modeling (protein docking) is to select the most accurate models among many models that are usually generated by a docking method. This article reviews advances in protein docking model assessment methods, focusing on recent developments that apply deep learning to several network architectures.
Collapse
Affiliation(s)
- Yuanyuan Zhang
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Xiao Wang
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Zicong Zhang
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Yunhan Huang
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN, USA.
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
| |
Collapse
|
15
|
Singh A, Copeland MM, Kundrotas PJ, Vakser IA. GRAMM Web Server for Protein Docking. Methods Mol Biol 2024; 2714:101-112. [PMID: 37676594 DOI: 10.1007/978-1-0716-3441-7_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
Prediction of the structure of protein complexes by docking methods is a well-established research field. The intermolecular energy landscapes in protein-protein interactions can be used to refine docking predictions and to detect macro-characteristics, such as the binding funnel. A new GRAMM web server for protein docking predicts a spectrum of docking poses that characterize the intermolecular energy landscape in protein interaction. A user-friendly interface provides options to choose free or template-based docking, as well as other advanced features, such as clustering of the docking poses, and interactive visualization of the docked models.
Collapse
Affiliation(s)
- Amar Singh
- Computational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, KS, USA
| | - Matthew M Copeland
- Computational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, KS, USA
| | - Petras J Kundrotas
- Computational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, KS, USA.
| | - Ilya A Vakser
- Computational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, KS, USA.
| |
Collapse
|
16
|
Krupa MA, Krupa P. Free-Docking and Template-Based Docking: Physics Versus Knowledge-Based Docking. Methods Mol Biol 2024; 2780:27-41. [PMID: 38987462 DOI: 10.1007/978-1-0716-3985-6_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2024]
Abstract
Docking methods can be used to predict the orientations of two or more molecules with respect of each other using a plethora of various algorithms, which can be based on the physics of interactions or can use information from databases and templates. The usability of these approaches depends on the type and size of the molecules, whose relative orientation will be estimated. The two most important limitations are (i) the computational cost of the prediction and (ii) the availability of the structural information for similar complexes. In general, if there is enough information about similar systems, knowledge-based and template-based methods can significantly reduce the computational cost while providing high accuracy of the prediction. However, if the information about the system topology and interactions between its partners is scarce, physics-based methods are more reliable or even the only choice. In this chapter, knowledge-, template-, and physics-based methods will be compared and briefly discussed providing examples of their usability with a special emphasis on physics-based protein-protein, protein-peptide, and protein-fullerene docking in the UNRES coarse-grained model.
Collapse
Affiliation(s)
- Magdalena A Krupa
- Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland
| | - Paweł Krupa
- Institute of Physics, Polish Academy of Sciences, Warsaw, Poland.
| |
Collapse
|
17
|
Asim A. Approaches to Backbone Flexibility in Protein-Protein Docking. Methods Mol Biol 2024; 2780:45-68. [PMID: 38987463 DOI: 10.1007/978-1-0716-3985-6_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2024]
Abstract
Proteins are the fundamental organic macromolecules in living systems that play a key role in a variety of biological functions including immunological detection, intracellular trafficking, and signal transduction. The docking of proteins has greatly advanced during recent decades and has become a crucial complement to experimental methods. Protein-protein docking is a helpful method for simulating protein complexes whose structures have not yet been solved experimentally. This chapter focuses on major search tactics along with various docking programs used in protein-protein docking algorithms, which include: direct search, exhaustive global search, local shape feature matching, randomized search, and broad category of post-docking approaches. As backbone flexibility predictions and interactions in high-resolution protein-protein docking remain important issues in the overall optimization context, we have put forward several methods and solutions used to handle backbone flexibility. In addition, various docking methods that are utilized for flexible backbone docking, including ATTRACT, FlexDock, FLIPDock, HADDOCK, RosettaDock, FiberDock, etc., along with their scoring functions, algorithms, advantages, and limitations are discussed. Moreover, what progress in search technology is expected, including not only the creation of new search algorithms but also the enhancement of existing ones, has been debated. As conformational flexibility is one of the most crucial factors affecting docking success, more work should be put into evaluating the conformational flexibility upon binding for a particular case in addition to developing new algorithms to replace the rigid body docking and scoring approach.
Collapse
Affiliation(s)
- Ayesha Asim
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modeling Laboratory, Faculty of Pharmacy, Medical University of Lublin, Lublin, Poland
| |
Collapse
|
18
|
Cieślak D, Kabelka I, Bartuzi D. Molecular Dynamics Simulations in Protein-Protein Docking. Methods Mol Biol 2024; 2780:91-106. [PMID: 38987465 DOI: 10.1007/978-1-0716-3985-6_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2024]
Abstract
Concerted interactions between all the cell components form the basis of biological processes. Protein-protein interactions (PPIs) constitute a tremendous part of this interaction network. Deeper insight into PPIs can help us better understand numerous diseases and lead to the development of new diagnostic and therapeutic strategies. PPI interfaces, until recently, were considered undruggable. However, it is now believed that the interfaces contain "hot spots," which could be targeted by small molecules. Such a strategy would require high-quality structural data of PPIs, which are difficult to obtain experimentally. Therefore, in silico modeling can complement or be an alternative to in vitro approaches. There are several computational methods for analyzing the structural data of the binding partners and modeling of the protein-protein dimer/oligomer structure. The major problem with in silico structure prediction of protein assemblies is obtaining sufficient sampling of protein dynamics. One of the methods that can take protein flexibility and the effects of the environment into account is Molecular Dynamics (MD). While sampling of the whole protein-protein association process with plain MD would be computationally expensive, there are several strategies to harness the method to PPI studies while maintaining reasonable use of resources. This chapter reviews known applications of MD in the PPI investigation workflows.
Collapse
Affiliation(s)
- Dominika Cieślak
- Laboratory of Plant Protein Phosphorylation, Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Warsaw, Poland
| | - Ivo Kabelka
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
| | - Damian Bartuzi
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden.
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modelling Lab, Medical University of Lublin, Lublin, Poland.
| |
Collapse
|
19
|
Kotev M, Diaz Gonzalez C. Molecular Dynamics and Other HPC Simulations for Drug Discovery. Methods Mol Biol 2024; 2716:265-291. [PMID: 37702944 DOI: 10.1007/978-1-0716-3449-3_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Abstract
High performance computing (HPC) is taking an increasingly important place in drug discovery. It makes possible the simulation of complex biochemical systems with high precision in a short time, thanks to the use of sophisticated algorithms. It promotes the advancement of knowledge in fields that are inaccessible or difficult to access through experimentation and it contributes to accelerating the discovery of drugs for unmet medical needs while reducing costs. Herein, we report how computational performance has evolved over the past years, and then we detail three domains where HPC is essential. Molecular dynamics (MD) is commonly used to explore the flexibility of proteins, thus generating a better understanding of different possible approaches to modulate their activity. Modeling and simulation of biopolymer complexes enables the study of protein-protein interactions (PPI) in healthy and disease states, thus helping the identification of targets of pharmacological interest. Virtual screening (VS) also benefits from HPC to predict in a short time, among millions or billions of virtual chemical compounds, the best potential ligands that will be tested in relevant assays to start a rational drug design process.
Collapse
Affiliation(s)
- Martin Kotev
- Evotec SE, Integrated Drug Discovery, Molecular Architects, Campus Curie, Toulouse, France
| | | |
Collapse
|
20
|
Khylyuk D. Protein-Protein Docking Approach to GPCR Oligomerization. Methods Mol Biol 2024; 2780:281-287. [PMID: 38987473 DOI: 10.1007/978-1-0716-3985-6_14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2024]
Abstract
G-protein-coupled receptors (GPCRs), the largest family of human membrane proteins, play a crucial role in cellular control and are the target of approximately one-third of all drugs on the market. Targeting these complexes with selectivity or formulating small molecules capable of modulating receptor-receptor interactions could potentially offer novel avenues for drug discovery, fostering the development of more refined and safer pharmacotherapies. Due to the lack of experimentally derived X-ray crystallography spectra of GPCR oligomers, there is growing evidence supporting the development of new in silico approaches for predicting GPCR self-assembling structures. The significance of GPCR oligomerization, the challenges in modeling these structures, and the potential of protein-protein docking algorithms to address these challenges are discussed. The study also underscores the use of various software solutions for modeling GPCR oligomeric structures and presents practical cases where these techniques have been successfully applied.
Collapse
Affiliation(s)
- Dmytro Khylyuk
- Chair and Department of Organic Chemistry , Medical University of Lublin, Lublin, Poland.
| |
Collapse
|
21
|
Gutierrez Guarnizo SA, Kellogg MK, Miller SC, Tikhonova E, Karamysheva ZN, Karamyshev AL. Pathogenic signal peptide variants in the human genome. NAR Genom Bioinform 2023; 5:lqad093. [PMID: 37859801 PMCID: PMC10583284 DOI: 10.1093/nargab/lqad093] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 09/05/2023] [Accepted: 09/29/2023] [Indexed: 10/21/2023] Open
Abstract
Secreted and membrane proteins represent a third of all cellular proteins and contain N-terminal signal peptides that are required for protein targeting to endoplasmic reticulum (ER). Mutations in signal peptides affect protein targeting, translocation, processing, and stability, and are associated with human diseases. However, only a few of them have been identified or characterized. In this report, we identified pathogenic signal peptide variants across the human genome using bioinformatic analyses and predicted the molecular mechanisms of their pathology. We recovered more than 65 thousand signal peptide mutations, over 11 thousand we classified as pathogenic, and proposed framework for distinction of their molecular mechanisms. The pathogenic mutations affect over 3.3 thousand genes coding for secreted and membrane proteins. Most pathogenic mutations alter the signal peptide hydrophobic core, a critical recognition region for the signal recognition particle, potentially activating the Regulation of Aberrant Protein Production (RAPP) quality control and specific mRNA degradation. The remaining pathogenic variants (about 25%) alter either the N-terminal region or signal peptidase processing site that can result in translocation deficiencies at the ER membrane or inhibit protein processing. This work provides a conceptual framework for the identification of mutations across the genome and their connection with human disease.
Collapse
Affiliation(s)
| | - Morgana K Kellogg
- Department of Cell Biology and Biochemistry, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
| | - Sarah C Miller
- Department of Cell Biology and Biochemistry, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
| | - Elena B Tikhonova
- Department of Cell Biology and Biochemistry, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
| | | | - Andrey L Karamyshev
- Department of Cell Biology and Biochemistry, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
| |
Collapse
|
22
|
Lynch VJ, Wagner GP. Cooption of polyalanine tract into a repressor domain in the mammalian transcription factor HoxA11. JOURNAL OF EXPERIMENTAL ZOOLOGY. PART B, MOLECULAR AND DEVELOPMENTAL EVOLUTION 2023; 340:486-495. [PMID: 34125492 DOI: 10.1002/jez.b.23063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 04/21/2021] [Accepted: 04/26/2021] [Indexed: 06/12/2023]
Abstract
An enduring problem in biology is explaining how novel functions of genes originated and how those functions diverge between species. Despite detailed studies on the functional evolution of a few proteins, the molecular mechanisms by which protein functions have evolved are almost entirely unknown. Here, we show that a polyalanine tract in the homeodomain transcription factor HoxA11 arose in the stem-lineage of mammals and functions as an autonomous repressor module by physically interacting with the PAH domains of SIN3 proteins. These results suggest that long polyalanine tracts, which are common in transcription factors and often associated with disease, may tend to function as repressor domains and can contribute to the diversification of transcription factor functions despite the deleterious consequences of polyalanine tract expansion.
Collapse
Affiliation(s)
- Vincent J Lynch
- Department of Biological Sciences, University at Buffalo, Buffalo, New York, USA
| | - Gunter P Wagner
- Department of Ecology and Evolutionary Biology and Yale Systems Biology Institute, Yale University, New Haven, Connecticut, USA
| |
Collapse
|
23
|
Jaiswal A, Roy R, Tamrakar A, Singh AK, Kar P, Kodgire P. Activation-induced cytidine deaminase an antibody diversification enzyme interacts with chromatin modifier UBN1 in B-cells. Sci Rep 2023; 13:19615. [PMID: 37949972 PMCID: PMC10638239 DOI: 10.1038/s41598-023-46448-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 11/01/2023] [Indexed: 11/12/2023] Open
Abstract
Activation-induced cytidine deaminase (AID) is the key mediator of antibody diversification in activated B-cells by the process of somatic hypermutation (SHM) and class switch recombination (CSR). Targeting AID to the Ig genes requires transcription (initiation and elongation), enhancers, and its interaction with numerous factors. Furthermore, the HIRA chaperon complex, a regulator of chromatin architecture, is indispensable for SHM. The HIRA chaperon complex consists of UBN1, ASF1a, HIRA, and CABIN1 that deposit H3.3 onto the DNA, the SHM hallmark. We explored whether UBN1 interacts with AID using computational and in-vitro experiments. Interestingly, our in-silico studies, such as molecular docking and molecular dynamics simulation results, predict that AID interacts with UBN1. Subsequently, co-immunoprecipitation and pull-down experiments established interactions between UBN1 and AID inside B-cells. Additionally, a double immunofluorescence assay confirmed that AID and UBN1 were co-localized in the human and chicken B-cell lines. Moreover, proximity ligation assay studies validated that AID interacts with UBN1. Ours is the first report on the interaction of genome mutator enzyme AID with UBN1. Nevertheless, the fate of interaction between UBN1 and AID is yet to be explored in the context of SHM or CSR.
Collapse
Affiliation(s)
- Ankit Jaiswal
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology, Indore, Madhya Pradesh, 453 552, India
| | - Rajarshi Roy
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology, Indore, Madhya Pradesh, 453 552, India
| | - Anubhav Tamrakar
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology, Indore, Madhya Pradesh, 453 552, India
| | - Amit Kumar Singh
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology, Indore, Madhya Pradesh, 453 552, India
| | - Parimal Kar
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology, Indore, Madhya Pradesh, 453 552, India
| | - Prashant Kodgire
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology, Indore, Madhya Pradesh, 453 552, India.
| |
Collapse
|
24
|
Christoffer C, Harini K, Archit G, Kihara D. Assembly of Protein Complexes In and On the Membrane with Predicted Spatial Arrangement Constraints. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.20.563303. [PMID: 37961264 PMCID: PMC10634698 DOI: 10.1101/2023.10.20.563303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Membrane proteins play crucial roles in various cellular processes, and their interactions with other proteins in and on the membrane are essential for their proper functioning. While an increasing number of structures of more membrane proteins are being determined, the available structure data is still sparse. To gain insights into the mechanisms of membrane protein complexes, computational docking methods are necessary due to the challenge of experimental determination. Here, we introduce Mem-LZerD, a rigid-body membrane docking algorithm designed to take advantage of modern membrane modeling and protein docking techniques to facilitate the docking of membrane protein complexes. Mem-LZerD is based on the LZerD protein docking algorithm, which has been constantly among the top servers in many rounds of CAPRI protein docking assessment. By employing a combination of geometric hashing, newly constrained by the predicted membrane height and tilt angle, and model scoring accounting for the energy of membrane insertion, we demonstrate the capability of Mem-LZerD to model diverse membrane protein-protein complexes. Mem-LZerD successfully performed unbound docking on 13 of 21 (61.9%) transmembrane complexes in an established benchmark, more than shown by previous approaches. It was additionally tested on new datasets of 44 transmembrane complexes and 92 peripheral membrane protein complexes, of which it successfully modeled 35 (79.5%) and 15 (16.3%) complexes respectively. When non-blind orientations of peripheral targets were included, the number of successes increased to 54 (58.7%). We further demonstrate that Mem-LZerD produces complex models which are suitable for molecular dynamics simulation. Mem-LZerD is made available at https://lzerd.kiharalab.org.
Collapse
Affiliation(s)
- Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
| | - Kannan Harini
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Gupta Archit
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
- Department of Genetic Engineering, SRM Institute of Science and Technology, Kattankulathur 603203, India
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
- Purdue University Center for Cancer Research, Purdue University, West Lafayette, IN, 47907, USA
| |
Collapse
|
25
|
Zhang L, Wang S, Hou J, Si D, Zhu J, Cao R. ComplexQA: a deep graph learning approach for protein complex structure assessment. Brief Bioinform 2023; 24:bbad287. [PMID: 37930021 DOI: 10.1093/bib/bbad287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 05/09/2023] [Accepted: 07/24/2023] [Indexed: 11/07/2023] Open
Abstract
MOTIVATION In recent years, the end-to-end deep learning method for single-chain protein structure prediction has achieved high accuracy. For example, the state-of-the-art method AlphaFold, developed by Google, has largely increased the accuracy of protein structure predictions to near experimental accuracy in some of the cases. At the same time, there are few methods that can evaluate the quality of protein complexes at the residue level. In particular, evaluating the quality of residues at the interface of protein complexes can lead to a wide range of applications, such as protein function analysis and drug design. In this paper, we introduce a new deep graph neural network-based method ComplexQA, to evaluate the local quality of interfaces for protein complexes by utilizing the residue-level structural information in 3D space and the sequence-level constraints. RESULTS We benchmark our method to other state-of-the-art quality assessment approaches on the HAF2 and DBM55-AF2 datasets (high-quality structural models predicted by AlphaFold-Multimer), and the BM5 docking dataset. The experimental results show that our proposed method achieves better or similar performance compared with other state-of-the-art methods, especially on difficult targets which only contain a few acceptable models. Our method is able to suggest a score for each interfac e residue, which demonstrates a powerful assessment tool for the ever-increasing number of protein complexes. AVAILABILITY https://github.com/Cao-Labs/ComplexQA.git. Contact: caora@plu.edu.
Collapse
Affiliation(s)
- Lei Zhang
- Department of Computer Science and Technology, AnHui University, Hefei, 230601, Anhui, China
| | - Sheng Wang
- Department of Computer Science and Technology, AnHui University, Hefei, 230601, Anhui, China
| | - Jie Hou
- Department of Computer Science, Saint Louis University, Saint. Louis, 63103, MO, USA
| | - Dong Si
- Division of Computing and Software Systems, University of Washington Bothell, Bothell, 98011, WA, USA
| | - Junyong Zhu
- Department of Computer Science and Technology, AnHui University, Hefei, 230601, Anhui, China
| | - Renzhi Cao
- Department of Humanities, Pacific Lutheran University, Tacoma, 98447, WA, USA
| |
Collapse
|
26
|
Mishra M, Jiang H, Wei Q. New insights on the differential interaction of sulfiredoxin with members of the peroxiredoxin family revealed by protein-protein docking and experimental studies. Eur J Pharmacol 2023; 954:175873. [PMID: 37353187 PMCID: PMC10426277 DOI: 10.1016/j.ejphar.2023.175873] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 06/11/2023] [Accepted: 06/20/2023] [Indexed: 06/25/2023]
Abstract
Sulfiredoxin (Srx) is the enzyme that restores the peroxidase activity of peroxiredoxins (Prxs) through catalyzing the reduction of hyperoxidized Prxs back to their active forms. This process involves protein-protein interaction in an enzyme-substrate binding manner. The integrity of the Srx-Prx axis contributes to the pathogenesis of various oxidative stress related human disorders including cancer, inflammation, cardiovascular and neurological diseases. The purpose of this study is to understand the structural and molecular biology of the Srx-Prx interaction, which may be of significance for prediction of target site for the novel drug-discovery. Homology modeling and protein-protein docking approaches were applied to examine the Srx-Prx interaction using online platforms including ITASSER, Phyre2, Swissmodel, AlphaFold, MZDOCK and ZDOCK. By in-silico studies, A 26-amino acid motif at the C-terminus of Prx1 was predicted to cause a steric hindrance for the kinetics of the Srx-Prx1 interaction. These predictions were tested in-vitro using purified recombinant proteins including Srx, full-length Prxs, and C-terminus deleted Prxs. We confirmed that deletion of the C-terminus of Prxs significantly enhanced its rate of association with Srx (i.e. >1000 fold increase in the ka of the Srx-Prx1 interaction) with minimal effect on the rate of dissociation (kd). Differential interaction of Srx with individual members of the Prx family was further examined in cultured cells. Taken together, these data add novel molecular and structural insights critical for the understanding of the biology of the Srx-Prx interaction that may be of value for the development of targeted therapy for human disorders.
Collapse
Affiliation(s)
- Murli Mishra
- Department of Toxicology and Cancer Biology, USA
| | - Hong Jiang
- Department of Toxicology and Cancer Biology, USA
| | - Qiou Wei
- Department of Toxicology and Cancer Biology, USA; Markey Cancer Center, University of Kentucky College of Medicine, Lexington, KY, 40536, USA.
| |
Collapse
|
27
|
Mijit A, Wang X, Li Y, Xu H, Chen Y, Xue W. Mapping synthetic binding proteins epitopes on diverse protein targets by protein structure prediction and protein-protein docking. Comput Biol Med 2023; 163:107183. [PMID: 37352638 DOI: 10.1016/j.compbiomed.2023.107183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 06/12/2023] [Accepted: 06/13/2023] [Indexed: 06/25/2023]
Abstract
Synthetic binding proteins (SBPs) are a class of artificial proteins engineered from privileged protein scaffolds, which can form highly specific molecular recognition interfaces with a variety of targets. Due to the characteristics of small size, high stability, and good tissue permeability, SBPs have important applications in biomedical research, disease diagnosis and treatment. However, knowledge of SBPs epitopes on the structures of target proteins is still limited, which hinder the development of novel SBPs. In this study, based on the currently available information of SBPs and their targets, 96 pairs of interacting proteins referring to 96 representative SBPs and 80 different targets, were systemically investigated using the state-of-the-art computational modeling techniques including AlphaFold2 protein structure prediction and Rosetta protein-protein docking. As a result, 71 out of the 96 pairs were successfully docked, of which 18, 33, and 20 pairs were defined as models with high, medium, and acceptable quality, respectively. In addition, the interface information was analyzed to decipher the interaction types driven SBPs and targets recognition. Overall, this work not only provides important structural information for understanding the mechanism of action of other SBPs with same protein scaffold, but also for aiding the rational protein engineering and to design of novel SBPs with biomedical applications.
Collapse
Affiliation(s)
- Arzu Mijit
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing, 401331, China
| | - Xiaona Wang
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing, 401331, China
| | - Yanlin Li
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing, 401331, China
| | - Hangwei Xu
- School of Medicine, Hangzhou City University, Hangzhou, 310000, China
| | - Yingjun Chen
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing, 401331, China.
| | - Weiwei Xue
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing, 401331, China.
| |
Collapse
|
28
|
Christoffer C, Kihara D. Modeling protein-nucleic acid complexes with extremely large conformational changes using Flex-LZerD. Proteomics 2023; 23:e2200322. [PMID: 36529945 PMCID: PMC10448949 DOI: 10.1002/pmic.202200322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 12/08/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022]
Abstract
Proteins and nucleic acids are key components in many processes in living cells, and interactions between proteins and nucleic acids are often crucial pathway components. In many cases, large flexibility of proteins as they interact with nucleic acids is key to their function. To understand the mechanisms of these processes, it is necessary to consider the 3D atomic structures of such protein-nucleic acid complexes. When such structures are not yet experimentally determined, protein docking can be used to computationally generate useful structure models. However, such docking has long had the limitation that the consideration of flexibility is usually limited to small movements or to small structures. We previously developed a method of flexible protein docking which could model ordered proteins which undergo large-scale conformational changes, which we also showed was compatible with nucleic acids. Here, we elaborate on the ability of that pipeline, Flex-LZerD, to model specifically interactions between proteins and nucleic acids, and demonstrate that Flex-LZerD can model more interactions and types of conformational change than previously shown.
Collapse
Affiliation(s)
- Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, USA
- Purdue University Center for Cancer Research, Purdue University, West Lafayette, Indiana, USA
| |
Collapse
|
29
|
Kang YA, Kim YJ, Jin SK, Choi HJ. Antioxidant, Collagenase Inhibitory, and Antibacterial Effects of Bioactive Peptides Derived from Enzymatic Hydrolysate of Ulva australis. Mar Drugs 2023; 21:469. [PMID: 37755082 PMCID: PMC10532848 DOI: 10.3390/md21090469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 08/21/2023] [Accepted: 08/23/2023] [Indexed: 09/28/2023] Open
Abstract
The protein extract of Ulva australis hydrolyzed with Alcalase and Flavourzyme was found to have multi-functional properties, including total antioxidant capacity (TAC), collagenase inhibitory, and antibacterial activities. The #5 fraction (SP5) and #7 fraction (SP7) of U. australis hydrolysate from cation-exchange chromatography displayed significantly high TAC, collagenase inhibitory, and antibacterial effects against Propionibacterium acnes, and only the Q3 fraction from anion-exchange chromatography displayed high multi-functional activities. Eight of 42 peptides identified by MALDI-TOF/MS and Q-TOF/MS/MS were selected from the results for screening with molecular docking on target proteins and were then synthesized. Thr-Gly-Thr-Trp (TGTW) displayed ABTS [2,2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid)] radical scavenging activity. The effect of TAC as Trolox equivalence was dependent on the concentration of TGTW. Asn-Arg-Asp-Tyr (NRDY) and Arg-Asp-Arg-Phe (RDRF) exhibited collagenase inhibitory activity, which increased according to the increase in concentration, and their IC50 values were 0.95 mM and 0.84 mM, respectively. Peptides RDRF and His-Ala-Val-Tyr (HAVY) displayed anti-P. Acnes effects, with IC50 values of 8.57 mM and 13.23 mM, respectively. These results suggest that the U. australis hydrolysate could be a resource for the application of effective nutraceuticals and cosmetics.
Collapse
Affiliation(s)
- You-An Kang
- Korea Beauty Industry Development Institute Co., Ltd., #501, Elite Bldg, Jeju Science Park, Cheomdanro 213-4, Jeju 63309, Republic of Korea;
| | - Ye-Jin Kim
- Oceanpep Co., Ltd., 105, Jinju Bioindustry Foundation, Musan-myeon, Jinju 52839, Republic of Korea;
| | - Sang-Keun Jin
- Division of Animal Science, Gyeongsang National University, Jinju 52828, Republic of Korea;
| | - Hwa-Jung Choi
- Department of Beauty Art, Youngsan University, 142 Bansong Beltway (Bansong-dong), Busan 48015, Republic of Korea
| |
Collapse
|
30
|
Abstract
A survey of protein databases indicates that the majority of enzymes exist in oligomeric forms, with about half of those found in the UniProt database being homodimeric. Understanding why many enzymes are in their dimeric form is imperative. Recent developments in experimental and computational techniques have allowed for a deeper comprehension of the cooperative interactions between the subunits of dimeric enzymes. This review aims to succinctly summarize these recent advancements by providing an overview of experimental and theoretical methods, as well as an understanding of cooperativity in substrate binding and the molecular mechanisms of cooperative catalysis within homodimeric enzymes. Focus is set upon the beneficial effects of dimerization and cooperative catalysis. These advancements not only provide essential case studies and theoretical support for comprehending dimeric enzyme catalysis but also serve as a foundation for designing highly efficient catalysts, such as dimeric organic catalysts. Moreover, these developments have significant implications for drug design, as exemplified by Paxlovid, which was designed for the homodimeric main protease of SARS-CoV-2.
Collapse
Affiliation(s)
- Ke-Wei Chen
- Lab of Computional Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Tian-Yu Sun
- Shenzhen Bay Laboratory, Shenzhen 518132, China
| | - Yun-Dong Wu
- Lab of Computional Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
- Shenzhen Bay Laboratory, Shenzhen 518132, China
| |
Collapse
|
31
|
Kishore V, Gaiwala Sharma SS, Raghunand TR. Septum site placement in Mycobacteria - identification and characterisation of mycobacterial homologues of Escherichia coli MinD. MICROBIOLOGY (READING, ENGLAND) 2023; 169:001359. [PMID: 37526955 PMCID: PMC10482377 DOI: 10.1099/mic.0.001359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 06/22/2023] [Indexed: 08/02/2023]
Abstract
A major virulence trait of Mycobacterium tuberculosis (M. tb) is its ability to enter a dormant state within its human host. Since cell division is intimately linked to metabolic shut down, understanding the mechanism of septum formation and its integration with other events in the division pathway is likely to offer clues to the molecular basis of dormancy. The M. tb genome lacks obvious homologues of several conserved cell division proteins, and this study was aimed at identifying and functionally characterising mycobacterial homologues of the E. coli septum site specification protein MinD (Ec MinD). Sequence homology based analyses suggested that the genomes of both M. tb and the saprophyte Mycobacterium smegmatis (M. smegmatis) encode two putative Ec MinD homologues - Rv1708/MSMEG_3743 and Rv3660c/ MSMEG_6171. Of these, Rv1708/MSMEG_3743 were found to be the true homologues, through complementation of the E. coli ∆minDE mutant HL1, overexpression studies, and structural comparisons. Rv1708 and MSMEG_3743 fully complemented the mini-cell phenotype of HL1, and over-expression of MSMEG_3743 in M. smegmatis led to cell elongation and a drastic decrease in c.f.u. counts, indicating its essentiality in cell-division. MSMEG_3743 displayed ATPase activity, consistent with its containing a conserved Walker A motif. Interaction of Rv1708 with the chromosome associated proteins ScpA and ParB, implied a link between its septum formation role, and chromosome segregation. Comparative structural analyses showed Rv1708 to be closer in similarity to Ec MinD than Rv3660c. In summary we identify Rv1708 and MSMEG_3743 to be homologues of Ec MinD, adding a critical missing piece to the mycobacterial cell division puzzle.
Collapse
Affiliation(s)
- Vimal Kishore
- CSIR - Centre for Cellular and Molecular Biology, Uppal Road Hyderabad - 500007, India
- Present address: National Centre for Cell Science (NCCS), NCCS Complex, University of Pune Campus, Pune University Rd, Ganeshkhind, Pune, 411007, India
| | - Sujata S. Gaiwala Sharma
- CSIR - Centre for Cellular and Molecular Biology, Uppal Road Hyderabad - 500007, India
- Present address: Indian Institute of Science Education and Research, Dr. Homi Bhabha Road, Pune 411008, India
| | - Tirumalai R. Raghunand
- CSIR - Centre for Cellular and Molecular Biology, Uppal Road Hyderabad - 500007, India
- Academy of Scientific and Innovative Research, Ghaziabad, India
| |
Collapse
|
32
|
Huttanus HM, Triola EKH, Velasquez-Guzman JC, Shin SM, Granja-Travez RS, Singh A, Dale T, Jha RK. Targeted mutagenesis and high-throughput screening of diversified gene and promoter libraries for isolating gain-of-function mutations. Front Bioeng Biotechnol 2023; 11:1202388. [PMID: 37545889 PMCID: PMC10400447 DOI: 10.3389/fbioe.2023.1202388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 06/25/2023] [Indexed: 08/08/2023] Open
Abstract
Targeted mutagenesis of a promoter or gene is essential for attaining new functions in microbial and protein engineering efforts. In the burgeoning field of synthetic biology, heterologous genes are expressed in new host organisms. Similarly, natural or designed proteins are mutagenized at targeted positions and screened for gain-of-function mutations. Here, we describe methods to attain complete randomization or controlled mutations in promoters or genes. Combinatorial libraries of one hundred thousands to tens of millions of variants can be created using commercially synthesized oligonucleotides, simply by performing two rounds of polymerase chain reactions. With a suitably engineered reporter in a whole cell, these libraries can be screened rapidly by performing fluorescence-activated cell sorting (FACS). Within a few rounds of positive and negative sorting based on the response from the reporter, the library can rapidly converge to a few optimal or extremely rare variants with desired phenotypes. Library construction, transformation and sequence verification takes 6-9 days and requires only basic molecular biology lab experience. Screening the library by FACS takes 3-5 days and requires training for the specific cytometer used. Further steps after sorting, including colony picking, sequencing, verification, and characterization of individual clones may take longer, depending on number of clones and required experiments.
Collapse
Affiliation(s)
- Herbert M. Huttanus
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, United States
- Agile BioFoundry, Emeryville, CA, United States
| | - Ellin-Kristina H. Triola
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, United States
- Agile BioFoundry, Emeryville, CA, United States
| | - Jeanette C. Velasquez-Guzman
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, United States
- Agile BioFoundry, Emeryville, CA, United States
| | - Sang-Min Shin
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, United States
- BOTTLE Consortium, Golden, CO, United States
| | - Rommel S. Granja-Travez
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, United States
- BOTTLE Consortium, Golden, CO, United States
| | - Anmoldeep Singh
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, United States
| | - Taraka Dale
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, United States
- Agile BioFoundry, Emeryville, CA, United States
- BOTTLE Consortium, Golden, CO, United States
| | - Ramesh K. Jha
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, United States
- Agile BioFoundry, Emeryville, CA, United States
- BOTTLE Consortium, Golden, CO, United States
| |
Collapse
|
33
|
Yu W, Zhou R, Li N, Lei ZC, Guo D, Peng F, Li Y, Bai X, Feng S, Wang Y, He J, Yin S, Zeng X, He L, Gao Y, Li M, Guo YR, Liu K, Wang Y. Histone tyrosine sulfation by SULT1B1 regulates H4R3me2a and gene transcription. Nat Chem Biol 2023; 19:855-864. [PMID: 36805701 DOI: 10.1038/s41589-023-01267-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 01/16/2023] [Indexed: 02/22/2023]
Abstract
Tyrosine sulfation is a common posttranslational modification in mammals. To date, it has been thought to be limited to secreted and transmembrane proteins, but little is known about tyrosine sulfation on nuclear proteins. Here we report that SULT1B1 is a histone sulfotransferase that can sulfate the tyrosine 99 residue of nascent histone H3 in cytosol. The sulfated histone H3 can be transported into the nucleus and majorly deposited in the promoter regions of genes in chromatin. While the H3Y99 residue is buried inside octameric nucleosome, dynamically regulated subnucleosomal structures provide chromatin-H3Y99sulf the opportunity of being recognized and bound by PRMT1, which deposits H4R3me2a in chromatin. Disruption of H3Y99sulf reduces PRMT1 binding to chromatin, H4R3me2a level and gene transcription. These findings reveal the mechanisms underlying H3Y99 sulfation and its cross-talk with H4R3me2a to regulate gene transcription. This study extends the spectrum of tyrosine sulfation on nuclear proteins and the repertoire of histone modifications regulating chromatin functions.
Collapse
Affiliation(s)
- Weixing Yu
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Runxin Zhou
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Nan Li
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhi-Chao Lei
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Dingyuan Guo
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fei Peng
- Department of Pathogen Biology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yan Li
- Department of Pathogen Biology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xue Bai
- Mass Spectrometry & Metabolomics Core Facility, The Biomedical Research Core Facility, Center for Research Equipment and Facilities, Westlake University, Hangzhou, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
| | - Shan Feng
- Mass Spectrometry & Metabolomics Core Facility, The Biomedical Research Core Facility, Center for Research Equipment and Facilities, Westlake University, Hangzhou, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
| | - Yu Wang
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jie He
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Sibi Yin
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiao Zeng
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Leya He
- Department of Gastrointestinal Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuan Gao
- Institute of Precision Medicine, Jining Medical University, Jining, China
| | - Mingchang Li
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yusong R Guo
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ke Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.
| | - Yugang Wang
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
- Cell Architecture Research Center, Huazhong University of Science and Technology, Wuhan, China.
| |
Collapse
|
34
|
Shuvo MH, Karim M, Roche R, Bhattacharya D. PIQLE: protein-protein interface quality estimation by deep graph learning of multimeric interaction geometries. BIOINFORMATICS ADVANCES 2023; 3:vbad070. [PMID: 37351310 PMCID: PMC10281963 DOI: 10.1093/bioadv/vbad070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 05/17/2023] [Accepted: 06/01/2023] [Indexed: 06/24/2023]
Abstract
Motivation Accurate modeling of protein-protein interaction interface is essential for high-quality protein complex structure prediction. Existing approaches for estimating the quality of a predicted protein complex structural model utilize only the physicochemical properties or energetic contributions of the interacting atoms, ignoring evolutionarily information or inter-atomic multimeric geometries, including interaction distance and orientations. Results Here, we present PIQLE, a deep graph learning method for protein-protein interface quality estimation. PIQLE leverages multimeric interaction geometries and evolutionarily information along with sequence- and structure-derived features to estimate the quality of individual interactions between the interfacial residues using a multi-head graph attention network and then probabilistically combines the estimated quality for scoring the overall interface. Experimental results show that PIQLE consistently outperforms existing state-of-the-art methods including DProQA, TRScore, GNN-DOVE and DOVE on multiple independent test datasets across a wide range of evaluation metrics. Our ablation study and comparison with the self-assessment module of AlphaFold-Multimer repurposed for protein complex scoring reveal that the performance gains are connected to the effectiveness of the multi-head graph attention network in leveraging multimeric interaction geometries and evolutionary information along with other sequence- and structure-derived features adopted in PIQLE. Availability and implementation An open-source software implementation of PIQLE is freely available at https://github.com/Bhattacharya-Lab/PIQLE. Supplementary information Supplementary data are available at Bioinformatics Advances online.
Collapse
Affiliation(s)
- Md Hossain Shuvo
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA
| | - Mohimenul Karim
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA
| | - Rahmatullah Roche
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA
| | | |
Collapse
|
35
|
Sleutel M, Pradhan B, Volkov AN, Remaut H. Structural analysis and architectural principles of the bacterial amyloid curli. Nat Commun 2023; 14:2822. [PMID: 37198180 DOI: 10.1038/s41467-023-38204-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 04/20/2023] [Indexed: 05/19/2023] Open
Abstract
Two decades have passed since the initial proposition that amyloids are not only (toxic) byproducts of an unintended aggregation cascade, but that they can also be produced by an organism to serve a defined biological function. That revolutionary idea was borne out of the realization that a large fraction of the extracellular matrix that holds Gram-negative cells into a persistent biofilm is composed of protein fibers (curli; tafi) with cross-β architecture, nucleation-dependent polymerization kinetics and classic amyloid tinctorial properties. The list of proteins shown to form so-called functional amyloid fibers in vivo has greatly expanded over the years, but detailed structural insights have not followed at a similar pace in part due to the associated experimental barriers. Here we combine extensive AlphaFold2 modelling and cryo-electron transmission microscopy to propose an atomic model of curli protofibrils, and their higher modes of organization. We uncover an unexpected structural diversity of curli building blocks and fibril architectures. Our results allow for a rationalization of the extreme physico-chemical robustness of curli, as well as earlier observations of inter-species curli promiscuity, and should facilitate further engineering efforts to expand the repertoire of curli-based functional materials.
Collapse
Affiliation(s)
- Mike Sleutel
- Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium.
- Structural and Molecular Microbiology, VIB-VUB Center for Structural Biology, Brussels, Belgium.
| | - Brajabandhu Pradhan
- Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium
- Structural and Molecular Microbiology, VIB-VUB Center for Structural Biology, Brussels, Belgium
| | - Alexander N Volkov
- Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium
- Jean Jeener NMR Center, Brussel, Belgium
| | - Han Remaut
- Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium.
- Structural and Molecular Microbiology, VIB-VUB Center for Structural Biology, Brussels, Belgium.
| |
Collapse
|
36
|
Gollmann-Tepeköylü C, Graber M, Hirsch J, Mair S, Naschberger A, Pölzl L, Nägele F, Kirchmair E, Degenhart G, Demetz E, Hilbe R, Chen HY, Engert JC, Böhm A, Franz N, Lobenwein D, Lener D, Fuchs C, Weihs A, Töchterle S, Vogel GF, Schweiger V, Eder J, Pietschmann P, Seifert M, Kronenberg F, Coassin S, Blumer M, Hackl H, Meyer D, Feuchtner G, Kirchmair R, Troppmair J, Krane M, Weiss G, Tsimikas S, Thanassoulis G, Grimm M, Rupp B, Huber LA, Zhang SY, Casanova JL, Tancevski I, Holfeld J. Toll-Like Receptor 3 Mediates Aortic Stenosis Through a Conserved Mechanism of Calcification. Circulation 2023; 147:1518-1533. [PMID: 37013819 PMCID: PMC10192061 DOI: 10.1161/circulationaha.122.063481] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 03/08/2023] [Indexed: 04/05/2023]
Abstract
BACKGROUND Calcific aortic valve disease (CAVD) is characterized by a phenotypic switch of valvular interstitial cells to bone-forming cells. Toll-like receptors (TLRs) are evolutionarily conserved pattern recognition receptors at the interface between innate immunity and tissue repair. Type I interferons (IFNs) are not only crucial for an adequate antiviral response but also implicated in bone formation. We hypothesized that the accumulation of endogenous TLR3 ligands in the valvular leaflets may promote the generation of osteoblast-like cells through enhanced type I IFN signaling. METHODS Human valvular interstitial cells isolated from aortic valves were challenged with mechanical strain or synthetic TLR3 agonists and analyzed for bone formation, gene expression profiles, and IFN signaling pathways. Different inhibitors were used to delineate the engaged signaling pathways. Moreover, we screened a variety of potential lipids and proteoglycans known to accumulate in CAVD lesions as potential TLR3 ligands. Ligand-receptor interactions were characterized by in silico modeling and verified through immunoprecipitation experiments. Biglycan (Bgn), Tlr3, and IFN-α/β receptor alpha chain (Ifnar1)-deficient mice and a specific zebrafish model were used to study the implication of the biglycan (BGN)-TLR3-IFN axis in both CAVD and bone formation in vivo. Two large-scale cohorts (GERA [Genetic Epidemiology Research on Adult Health and Aging], n=55 192 with 3469 aortic stenosis cases; UK Biobank, n=257 231 with 2213 aortic stenosis cases) were examined for genetic variation at genes implicated in BGN-TLR3-IFN signaling associating with CAVD in humans. RESULTS Here, we identify TLR3 as a central molecular regulator of calcification in valvular interstitial cells and unravel BGN as a new endogenous agonist of TLR3. Posttranslational BGN maturation by xylosyltransferase 1 (XYLT1) is required for TLR3 activation. Moreover, BGN induces the transdifferentiation of valvular interstitial cells into bone-forming osteoblasts through the TLR3-dependent induction of type I IFNs. It is intriguing that Bgn-/-, Tlr3-/-, and Ifnar1-/- mice are protected against CAVD and display impaired bone formation. Meta-analysis of 2 large-scale cohorts with >300 000 individuals reveals that genetic variation at loci relevant to the XYLT1-BGN-TLR3-interferon-α/β receptor alpha chain (IFNAR) 1 pathway is associated with CAVD in humans. CONCLUSIONS This study identifies the BGN-TLR3-IFNAR1 axis as an evolutionarily conserved pathway governing calcification of the aortic valve and reveals a potential therapeutic target to prevent CAVD.
Collapse
Affiliation(s)
| | - Michael Graber
- Department of Cardiac Surgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Jakob Hirsch
- Department of Cardiac Surgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Sophia Mair
- Department of Cardiac Surgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Andreas Naschberger
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
- Division of Biological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, 23955, Saudi Arabia
| | - Leo Pölzl
- Department of Cardiac Surgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Felix Nägele
- Department of Cardiac Surgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Elke Kirchmair
- Department of Cardiac Surgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Gerald Degenhart
- Department of Radiology, Core Facility for Micro-CT, Medical University of Innsbruck, Innsbruck, Austria
| | - Egon Demetz
- Department of Internal Medicine III, Medical University of Innsbruck, Innsbruck, Austria
| | - Richard Hilbe
- Department of Internal Medicine III, Medical University of Innsbruck, Innsbruck, Austria
| | - Hao-Yu Chen
- Preventive and Genomic Cardiology, McGill University Health Centre Research Institute, Montreal, QC, Canada
| | - James C. Engert
- Preventive and Genomic Cardiology, McGill University Health Centre Research Institute, Montreal, QC, Canada
| | - Anna Böhm
- Department of Internal Medicine III, Medical University of Innsbruck, Innsbruck, Austria
| | - Nadja Franz
- Department of Cardiac Surgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Daniela Lobenwein
- Department of Cardiac Surgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Daniela Lener
- Department of Internal Medicine III, Medical University of Innsbruck, Innsbruck, Austria
| | - Christiane Fuchs
- Department Life Science Engineering, University of Applied Sciences Technikum Wien, Vienna, Austria
| | - Anna Weihs
- Department Life Science Engineering, University of Applied Sciences Technikum Wien, Vienna, Austria
| | - Sonja Töchterle
- Institute of Molecular Biology/CMBI, University of Innsbruck, Innsbruck, Austria
| | - Georg F. Vogel
- Department of Pediatrics/Institute of Cell biology, Medical University of Innsbruck, Innsbruck, Austria
| | - Victor Schweiger
- Department of Cardiac Surgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Jonas Eder
- Department of Cardiac Surgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Peter Pietschmann
- Division of Cellular and Molecular Pathophysiology, Department of Pathophysiology and Allergy Research, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, Vienna, Austria
| | - Markus Seifert
- Department of Internal Medicine III, Medical University of Innsbruck, Innsbruck, Austria
| | - Florian Kronenberg
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Stefan Coassin
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Michael Blumer
- Institute of Clinical and Functional Anatomy, Innsbruck Medical University, Innsbruck, Austria
| | - Hubert Hackl
- Institute of Bioinformatics, Medical University of Innsbruck, Innsbruck, Austria
| | - Dirk Meyer
- Institute of Molecular Biology/CMBI, University of Innsbruck, Innsbruck, Austria
| | - Gudrun Feuchtner
- Department of Radiology, Core Facility for Micro-CT, Medical University of Innsbruck, Innsbruck, Austria
| | - Rudolf Kirchmair
- Department of Internal Medicine III, Medical University of Innsbruck, Innsbruck, Austria
| | - Jakob Troppmair
- Daniel Swarovski Research Laboratory, Department of Visceral, Transplant and Thoracic Surgery, University of Innsbruck, Innsbruck, Innsbruck, Austria
| | - Markus Krane
- Department of Cardiovascular Surgery, German Heart Center Munich at the Technical University Munich, Munich, Germany
| | - Günther Weiss
- Department of Internal Medicine III, Medical University of Innsbruck, Innsbruck, Austria
| | - Sotirios Tsimikas
- Division of Cardiovascular Diseases, University of California, San Diego, La Jolla, USA
| | - George Thanassoulis
- Preventive and Genomic Cardiology, McGill University Health Centre Research Institute, Montreal, QC, Canada
| | - Michael Grimm
- Department of Cardiac Surgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Bernhard Rupp
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Lukas A. Huber
- Institute of Cell Biology, Medical University of Innsbruck, Innsbruck, Austria
- Austrian Drug Screening Institute, ADSI, Innsbruck, Austria
| | - Shen-Ying Zhang
- St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY, USA
- Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM U1163, Necker Hospital for Sick Children, Paris, France
- University of Paris, Imagine Institute, Paris, France
| | - Jean-Laurent Casanova
- St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY, USA
- Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM U1163, Necker Hospital for Sick Children, Paris, France
- University of Paris, Imagine Institute, Paris, France
- Howard Hughes Medical Institute, New York, NY, USA
| | - Ivan Tancevski
- Department of Internal Medicine III, Medical University of Innsbruck, Innsbruck, Austria
| | - Johannes Holfeld
- Department of Cardiac Surgery, Medical University of Innsbruck, Innsbruck, Austria
| |
Collapse
|
37
|
Yang S, Gong W, Zhou T, Sun X, Chen L, Zhou W, Li C. emPDBA: protein-DNA binding affinity prediction by combining features from binding partners and interface learned with ensemble regression model. Brief Bioinform 2023:7165253. [PMID: 37193676 DOI: 10.1093/bib/bbad192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 04/26/2023] [Accepted: 04/29/2023] [Indexed: 05/18/2023] Open
Abstract
Protein-deoxyribonucleic acid (DNA) interactions are important in a variety of biological processes. Accurately predicting protein-DNA binding affinity has been one of the most attractive and challenging issues in computational biology. However, the existing approaches still have much room for improvement. In this work, we propose an ensemble model for Protein-DNA Binding Affinity prediction (emPDBA), which combines six base models with one meta-model. The complexes are classified into four types based on the DNA structure (double-stranded or other forms) and the percentage of interface residues. For each type, emPDBA is trained with the sequence-based, structure-based and energy features from binding partners and complex structures. Through feature selection by the sequential forward selection method, it is found that there do exist considerable differences in the key factors contributing to intermolecular binding affinity. The complex classification is beneficial for the important feature extraction for binding affinity prediction. The performance comparison of our method with other peer ones on the independent testing dataset shows that emPDBA outperforms the state-of-the-art methods with the Pearson correlation coefficient of 0.53 and the mean absolute error of 1.11 kcal/mol. The comprehensive results demonstrate that our method has a good performance for protein-DNA binding affinity prediction. Availability and implementation: The source code is available at https://github.com/ChunhuaLiLab/emPDBA/.
Collapse
Affiliation(s)
- Shuang Yang
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Weikang Gong
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Tong Zhou
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Xiaohan Sun
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Lei Chen
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Wenxue Zhou
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Chunhua Li
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
| |
Collapse
|
38
|
Sheffler W, Yang EC, Dowling Q, Hsia Y, Fries CN, Stanislaw J, Langowski MD, Brandys M, Li Z, Skotheim R, Borst AJ, Khmelinskaia A, King NP, Baker D. Fast and versatile sequence-independent protein docking for nanomaterials design using RPXDock. PLoS Comput Biol 2023; 19:e1010680. [PMID: 37216343 PMCID: PMC10237659 DOI: 10.1371/journal.pcbi.1010680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 06/02/2023] [Accepted: 04/09/2023] [Indexed: 05/24/2023] Open
Abstract
Computationally designed multi-subunit assemblies have shown considerable promise for a variety of applications, including a new generation of potent vaccines. One of the major routes to such materials is rigid body sequence-independent docking of cyclic oligomers into architectures with point group or lattice symmetries. Current methods for docking and designing such assemblies are tailored to specific classes of symmetry and are difficult to modify for novel applications. Here we describe RPXDock, a fast, flexible, and modular software package for sequence-independent rigid-body protein docking across a wide range of symmetric architectures that is easily customizable for further development. RPXDock uses an efficient hierarchical search and a residue-pair transform (RPX) scoring method to rapidly search through multidimensional docking space. We describe the structure of the software, provide practical guidelines for its use, and describe the available functionalities including a variety of score functions and filtering tools that can be used to guide and refine docking results towards desired configurations.
Collapse
Affiliation(s)
- William Sheffler
- Institute for Protein Design, University of Washington, Seattle, Washington, United States of America
| | - Erin C. Yang
- Institute for Protein Design, University of Washington, Seattle, Washington, United States of America
- Department of Biochemistry, University of Washington, Seattle, Washington, United States of America
- Graduate Program in Biological Physics, Structure & Design, University of Washington, Seattle, Washington, United States of America
| | - Quinton Dowling
- Institute for Protein Design, University of Washington, Seattle, Washington, United States of America
- Department of Bioengineering, University of Washington, Seattle, Washington, United States of America
| | - Yang Hsia
- Institute for Protein Design, University of Washington, Seattle, Washington, United States of America
| | - Chelsea N. Fries
- Institute for Protein Design, University of Washington, Seattle, Washington, United States of America
| | - Jenna Stanislaw
- Institute for Protein Design, University of Washington, Seattle, Washington, United States of America
- Department of Biochemistry, University of Washington, Seattle, Washington, United States of America
- Transdisciplinary Research Area “Building Blocks of Matter and Fundamental Interactions (TRA Matter)”, University of Bonn, Bonn, Germany
- Life and Medical Sciences Institute, University of Bonn, Bonn, Germany
| | - Mark D. Langowski
- Institute for Protein Design, University of Washington, Seattle, Washington, United States of America
- Graduate Program in Molecular and Cellular Biology, University of Washington, Seattle, Washington, United States of America
| | - Marisa Brandys
- Institute for Protein Design, University of Washington, Seattle, Washington, United States of America
- Department of Biochemistry, University of Washington, Seattle, Washington, United States of America
| | - Zhe Li
- Institute for Protein Design, University of Washington, Seattle, Washington, United States of America
| | - Rebecca Skotheim
- Institute for Protein Design, University of Washington, Seattle, Washington, United States of America
| | - Andrew J. Borst
- Institute for Protein Design, University of Washington, Seattle, Washington, United States of America
- Department of Biochemistry, University of Washington, Seattle, Washington, United States of America
| | - Alena Khmelinskaia
- Institute for Protein Design, University of Washington, Seattle, Washington, United States of America
- Department of Biochemistry, University of Washington, Seattle, Washington, United States of America
- Transdisciplinary Research Area “Building Blocks of Matter and Fundamental Interactions (TRA Matter)”, University of Bonn, Bonn, Germany
- Life and Medical Sciences Institute, University of Bonn, Bonn, Germany
| | - Neil P. King
- Institute for Protein Design, University of Washington, Seattle, Washington, United States of America
- Department of Biochemistry, University of Washington, Seattle, Washington, United States of America
| | - David Baker
- Institute for Protein Design, University of Washington, Seattle, Washington, United States of America
- Department of Biochemistry, University of Washington, Seattle, Washington, United States of America
- Howard Hughes Medical Institute, University of Washington, Seattle, Washington, United States of America
| |
Collapse
|
39
|
Chothe SK, Jakka P, Boorla VS, Ramasamy S, Gontu A, Nissly RH, Brown J, Turner G, Sewall BJ, Reeder DM, Field KA, Engiles JB, Amirthalingam S, Ravichandran A, LaBella L, Nair MS, Maranas CD, Kuchipudi SV. Little Brown Bats ( Myotis lucifugus) Support the Binding of SARS-CoV-2 Spike and Are Likely Susceptible to SARS-CoV-2 Infection. Viruses 2023; 15:v15051103. [PMID: 37243189 DOI: 10.3390/v15051103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 04/25/2023] [Accepted: 04/28/2023] [Indexed: 05/28/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), believed to have originated from a bat species, can infect a wide range of non-human hosts. Bats are known to harbor hundreds of coronaviruses capable of spillover into human populations. Recent studies have shown a significant variation in the susceptibility among bat species to SARS-CoV-2 infection. We show that little brown bats (LBB) express angiotensin-converting enzyme 2 receptor and the transmembrane serine protease 2, which are accessible to and support SARS-CoV-2 binding. All-atom molecular dynamics (MD) simulations revealed that LBB ACE2 formed strong electrostatic interactions with the RBD similar to human and cat ACE2 proteins. In summary, LBBs, a widely distributed North American bat species, could be at risk of SARS-CoV-2 infection and potentially serve as a natural reservoir. Finally, our framework, combining in vitro and in silico methods, is a useful tool to assess the SARS-CoV-2 susceptibility of bats and other animal species.
Collapse
Affiliation(s)
- Shubhada K Chothe
- Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802, USA
- Center for Infectious Disease Dynamics, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
| | - Padmaja Jakka
- Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802, USA
- Center for Infectious Disease Dynamics, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
| | - Veda Sheersh Boorla
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Santhamani Ramasamy
- Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802, USA
- Center for Infectious Disease Dynamics, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
| | - Abhinay Gontu
- Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802, USA
- Center for Infectious Disease Dynamics, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
| | - Ruth H Nissly
- Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802, USA
- Center for Infectious Disease Dynamics, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
| | - Justin Brown
- Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802, USA
- Pennsylvania Game Commission, 2001 Elmerton Ave, Harrisburg, PA 17110, USA
| | - Gregory Turner
- Pennsylvania Game Commission, 2001 Elmerton Ave, Harrisburg, PA 17110, USA
| | - Brent J Sewall
- Department of Biology, Temple University, Philadelphia, PA 19122, USA
| | - DeeAnn M Reeder
- Department of Biology, Bucknell University, Lewisburg, PA 17837, USA
| | - Kenneth A Field
- Department of Biology, Bucknell University, Lewisburg, PA 17837, USA
| | - Julie B Engiles
- Departments of Pathobiology and Clinical Studies, New Bolton Center, School of Veterinary Medicine, University of Pennsylvania, Kennett Square, PA 19348, USA
| | - Saranya Amirthalingam
- Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802, USA
| | - Abirami Ravichandran
- Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802, USA
| | - Lindsey LaBella
- Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802, USA
- Center for Infectious Disease Dynamics, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
| | - Meera Surendran Nair
- Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802, USA
- Center for Infectious Disease Dynamics, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Suresh V Kuchipudi
- Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802, USA
- Center for Infectious Disease Dynamics, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
| |
Collapse
|
40
|
Papadopoulos N, Nédélec A, Derenne A, Şulea TA, Pecquet C, Chachoua I, Vertenoeil G, Tilmant T, Petrescu AJ, Mazzucchelli G, Iorga BI, Vertommen D, Constantinescu SN. Oncogenic CALR mutant C-terminus mediates dual binding to the thrombopoietin receptor triggering complex dimerization and activation. Nat Commun 2023; 14:1881. [PMID: 37019903 PMCID: PMC10076285 DOI: 10.1038/s41467-023-37277-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 03/04/2023] [Indexed: 04/07/2023] Open
Abstract
Calreticulin (CALR) frameshift mutations represent the second cause of myeloproliferative neoplasms (MPN). In healthy cells, CALR transiently and non-specifically interacts with immature N-glycosylated proteins through its N-terminal domain. Conversely, CALR frameshift mutants turn into rogue cytokines by stably and specifically interacting with the Thrombopoietin Receptor (TpoR), inducing its constitutive activation. Here, we identify the basis of the acquired specificity of CALR mutants for TpoR and define the mechanisms by which complex formation triggers TpoR dimerization and activation. Our work reveals that CALR mutant C-terminus unmasks CALR N-terminal domain, rendering it more accessible to bind immature N-glycans on TpoR. We further find that the basic mutant C-terminus is partially α-helical and define how its α-helical segment concomitantly binds acidic patches of TpoR extracellular domain and induces dimerization of both CALR mutant and TpoR. Finally, we propose a model of the tetrameric TpoR-CALR mutant complex and identify potentially targetable sites.
Collapse
Affiliation(s)
- Nicolas Papadopoulos
- Ludwig Institute for Cancer Research Brussels, Brussels, Belgium
- Université catholique de Louvain and de Duve Institute, Brussels, Belgium
| | - Audrey Nédélec
- Ludwig Institute for Cancer Research Brussels, Brussels, Belgium
- Université catholique de Louvain and de Duve Institute, Brussels, Belgium
| | - Allison Derenne
- Spectralys Biotech SRL, rue Auguste Piccard 48, 6041, Gosselies, Belgium
| | - Teodor Asvadur Şulea
- Department of Bioinformatics and Structural Biochemistry, Institute of Biochemistry of the Romanian Academy, Splaiul Independentei 296, Bucharest, 060031, Romania
| | - Christian Pecquet
- Ludwig Institute for Cancer Research Brussels, Brussels, Belgium
- Université catholique de Louvain and de Duve Institute, Brussels, Belgium
| | - Ilyas Chachoua
- Ludwig Institute for Cancer Research Brussels, Brussels, Belgium
- Université catholique de Louvain and de Duve Institute, Brussels, Belgium
- Department of Molecular Biology and Genetics, Bilkent University, Ankara, Turkey
| | - Gaëlle Vertenoeil
- Ludwig Institute for Cancer Research Brussels, Brussels, Belgium
- Université catholique de Louvain and de Duve Institute, Brussels, Belgium
| | - Thomas Tilmant
- Mass Spectrometry Laboratory, MolSys Research Unit, Universiy of Liège, 4000, Liège, Belgium
| | - Andrei-Jose Petrescu
- Department of Bioinformatics and Structural Biochemistry, Institute of Biochemistry of the Romanian Academy, Splaiul Independentei 296, Bucharest, 060031, Romania
| | - Gabriel Mazzucchelli
- Mass Spectrometry Laboratory, MolSys Research Unit, Universiy of Liège, 4000, Liège, Belgium
| | - Bogdan I Iorga
- Université Paris-Saclay, CNRS, Institut de Chimie des Substances Naturelles, UPR 2301, Gif-sur-Yvette, France
| | - Didier Vertommen
- Université catholique de Louvain and de Duve Institute, Brussels, Belgium
- de Duve Institute and MASSPROT platform, Brussels, Belgium
| | - Stefan N Constantinescu
- Ludwig Institute for Cancer Research Brussels, Brussels, Belgium.
- Université catholique de Louvain and de Duve Institute, Brussels, Belgium.
- Walloon Excelence in Life Sciences and Biotechnology, WELBIO, avenue Pasteur, 6, 1300, Wavre, Belgium.
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, Oxford University, Oxford, UK.
| |
Collapse
|
41
|
Dhusia K, Su Z, Wu Y. Computational analyses of the interactome between TNF and TNFR superfamilies. Comput Biol Chem 2023; 103:107823. [PMID: 36682326 DOI: 10.1016/j.compbiolchem.2023.107823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 01/05/2023] [Accepted: 01/18/2023] [Indexed: 01/20/2023]
Abstract
Proteins in the tumor necrosis factor (TNF) superfamily (TNFSF) regulate diverse cellular processes by interacting with their receptors in the TNF receptor (TNFR) superfamily (TNFRSF). Ligands and receptors in these two superfamilies form a complicated network of interactions, in which the same ligand can bind to different receptors and the same receptor can be shared by different ligands. In order to study these interactions on a systematic level, a TNFSF-TNFRSF interactome was constructed in this study by searching the database which consists of both experimentally measured and computationally predicted protein-protein interactions (PPIs). The interactome contains a total number of 194 interactions between 18 TNFSF ligands and 29 TNFRSF receptors in human. We modeled the structure for each ligand-receptor interaction in the network. Their binding affinities were further computationally estimated based on modeled structures. Our computational outputs, which are all publicly accessible, serve as a valuable addition to the currently limited experimental resources to study TNF-mediated cell signaling.
Collapse
Affiliation(s)
- Kalyani Dhusia
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, the United States of America
| | - Zhaoqian Su
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, the United States of America
| | - Yinghao Wu
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, the United States of America.
| |
Collapse
|
42
|
Tam C, Kukimoto-Niino M, Miyata-Yabuki Y, Tsuda K, Mishima-Tsumagari C, Ihara K, Inoue M, Yonemochi M, Hanada K, Matsumoto T, Shirouzu M, Zhang KYJ. Targeting Ras-binding domain of ELMO1 by computational nanobody design. Commun Biol 2023; 6:284. [PMID: 36932164 PMCID: PMC10023680 DOI: 10.1038/s42003-023-04657-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 03/02/2023] [Indexed: 03/19/2023] Open
Abstract
The control of cell movement through manipulation of cytoskeletal structure has therapeutic prospects notably in the development of novel anti-metastatic drugs. In this study, we determine the structure of Ras-binding domain (RBD) of ELMO1, a protein involved in cytoskeletal regulation, both alone and in complex with the activator RhoG and verify its targetability through computational nanobody design. Using our dock-and-design approach optimized with native-like initial pose selection, we obtain Nb01, a detectable binder from scratch in the first-round design. An affinity maturation step guided by structure-activity relationship at the interface generates 23 Nb01 sequence variants and 17 of them show enhanced binding to ELMO1-RBD and are modeled to form major spatial overlaps with RhoG. The best binder, Nb29, inhibited ELMO1-RBD/RhoG interaction. Molecular dynamics simulation of the flexibility of CDR2 and CDR3 of Nb29 reveal the design of stabilizing mutations at the CDR-framework junctions potentially confers the affinity enhancement.
Collapse
Affiliation(s)
- Chunlai Tam
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, 277-8561, Japan
| | - Mutsuko Kukimoto-Niino
- Laboratory for Protein Functional and Structural Biology, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan.
| | - Yukako Miyata-Yabuki
- Drug Discovery Structural Biology Platform Unit, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
| | - Kengo Tsuda
- Laboratory for Protein Functional and Structural Biology, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
| | - Chiemi Mishima-Tsumagari
- Laboratory for Protein Functional and Structural Biology, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
| | - Kentaro Ihara
- Laboratory for Protein Functional and Structural Biology, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
| | - Mio Inoue
- Laboratory for Protein Functional and Structural Biology, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
| | - Mayumi Yonemochi
- Drug Discovery Structural Biology Platform Unit, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
| | - Kazuharu Hanada
- Laboratory for Protein Functional and Structural Biology, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
| | - Takehisa Matsumoto
- Drug Discovery Structural Biology Platform Unit, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
| | - Mikako Shirouzu
- Laboratory for Protein Functional and Structural Biology, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
- Drug Discovery Structural Biology Platform Unit, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
| | - Kam Y J Zhang
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan.
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, 277-8561, Japan.
| |
Collapse
|
43
|
PIQLE: protein-protein interface quality estimation by deep graph learning of multimeric interaction geometries. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.14.528528. [PMID: 36824789 PMCID: PMC9949034 DOI: 10.1101/2023.02.14.528528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
Accurate modeling of protein-protein interaction interface is essential for high-quality protein complex structure prediction. Existing approaches for estimating the quality of a predicted protein complex structural model utilize only the physicochemical properties or energetic contributions of the interacting atoms, ignoring evolutionarily information or inter-atomic multimeric geometries, including interaction distance and orientations. Here we present PIQLE, a deep graph learning method for protein-protein interface quality estimation. PIQLE leverages multimeric interaction geometries and evolutionarily information along with sequence- and structure-derived features to estimate the quality of the individual interactions between the interfacial residues using a multihead graph attention network and then probabilistically combines the estimated quality of the interfacial residues for scoring the overall interface. Experimental results show that PIQLE consistently outperforms existing state-of-the-art methods on multiple independent test datasets across a wide range of evaluation metrics. Our ablation study reveals that the performance gains are connected to the effectiveness of the multihead graph attention network in leveraging multimeric interaction geometries and evolutionary information along with other sequence- and structure-derived features adopted in PIQLE. An open-source software implementation of PIQLE, licensed under the GNU General Public License v3, is freely available at https://github.com/Bhattacharya-Lab/PIQLE .
Collapse
|
44
|
Etemadi A, Karimi-Jafari MH, Negahdari B, Asgari Y, Reza Khorramizadeh M, Mohammadian F, Mazloomi M. Design of a dual-function agent by fusing a designed anti-VEGF-A binder and CPG-2 enzyme. J Biomol Struct Dyn 2023; 41:11463-11470. [PMID: 36629035 DOI: 10.1080/07391102.2022.2162584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 12/19/2022] [Indexed: 01/12/2023]
Abstract
Anti-VEGF therapies are common for the treatment of cancer. Carboxypeptidase G (CPG-2) enzyme is a zinc-dependent metalloenzyme that metabolizes non-toxic synthetic 'benzoic mustard prodrugs' to cytotoxic moieties in tumor cells. In this study, we designed a dual-activity agent by combining a designed anti-VEGF- and CPG-2 enzyme to convert methotrexate (MTX). VEGF-A was docked against a set of scaffolds, and suitable inverse rotamers were made. Rosetta design was used for the interface design. The top 1200 binders were chosen by flow cytometry and displayed in yeast. The activity of CPG-2 enzyme was analyzed at different temperature conditions and in the presence of the substrate, MTX. Optimal binders were selected and protein was eluted using immobilized metal affinity chromatography and size-exclusion chromatography. Both, native PAGE and on-yeast flow cytometry confirmed the binding of the binder to VEGF-A. The activity of truncated enzymes was slightly lower than that of full-length enzymes linked to VEGF-A. The method should be generally useful as a dual-activity agent for targeting VEGF-A and combination therapy with the enzyme CPG-2 for metabolizing non-toxic prodrugs to cytotoxic moieties.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Ali Etemadi
- Medical Biotechnology Department, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | | | - Babak Negahdari
- Medical Biotechnology Department, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Yazdan Asgari
- Medical Biotechnology Department, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Mohammad Reza Khorramizadeh
- Medical Biotechnology Department, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- Biosensor Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Farideh Mohammadian
- Department of Medical Biotechnology, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Mohammadali Mazloomi
- Medical Biotechnology Department, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| |
Collapse
|
45
|
Sánchez BJ, Mubaid S, Busque S, de los Santos Y, Ashour K, Sadek J, Lian X, Khattak S, Di Marco S, Gallouzi IE. The formation of HuR/YB1 complex is required for the stabilization of target mRNA to promote myogenesis. Nucleic Acids Res 2023; 51:1375-1392. [PMID: 36629268 PMCID: PMC9943665 DOI: 10.1093/nar/gkac1245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 12/14/2022] [Indexed: 01/12/2023] Open
Abstract
mRNA stability is the mechanism by which cells protect transcripts allowing their expression to execute various functions that affect cell metabolism and fate. It is well-established that RNA binding proteins (RBPs) such as HuR use their ability to stabilize mRNA targets to modulate vital processes such as muscle fiber formation (myogenesis). However, the machinery and the mechanisms regulating mRNA stabilization are still elusive. Here, we identified Y-Box binding protein 1 (YB1) as an indispensable HuR binding partner for mRNA stabilization and promotion of myogenesis. Both HuR and YB1 bind to 409 common mRNA targets, 147 of which contain a U-rich consensus motif in their 3' untranslated region (3'UTR) that can also be found in mRNA targets in other cell systems. YB1 and HuR form a heterodimer that associates with the U-rich consensus motif to stabilize key promyogenic mRNAs. The formation of this complex involves a small domain in HuR (227-234) that if mutated prevents HuR from reestablishing myogenesis in siHuR-treated muscle cells. Together our data uncover that YB1 is a key player in HuR-mediated stabilization of pro-myogenic mRNAs and provide the first indication that the mRNA stability mechanism is as complex as other key cellular processes such as mRNA decay and translation.
Collapse
Affiliation(s)
- Brenda Janice Sánchez
- KAUST Smart-Health Initiative King Abdullah University of Science and Technology (KAUST), Jeddah, Saudi Arabia,KAUST Biological Environmental Science and Engineering (BESE) Division, King Abdullah University of Science and Technology (KAUST), Jeddah, Saudi Arabia,Dept. of Biochemistry, McGill University, 3655 Promenade Sir William Osler, Montreal, QC H3G1Y6, Canada,Rosalind & Morris Goodman Cancer Institute, McGill University, 1160 Pine Avenue, Montreal, QC H3A1A3, Canada
| | - Souad Mubaid
- Dept. of Biochemistry, McGill University, 3655 Promenade Sir William Osler, Montreal, QC H3G1Y6, Canada,Rosalind & Morris Goodman Cancer Institute, McGill University, 1160 Pine Avenue, Montreal, QC H3A1A3, Canada
| | - Sandrine Busque
- Dept. of Biochemistry, McGill University, 3655 Promenade Sir William Osler, Montreal, QC H3G1Y6, Canada,Rosalind & Morris Goodman Cancer Institute, McGill University, 1160 Pine Avenue, Montreal, QC H3A1A3, Canada
| | - Yossef Lopez de los Santos
- KAUST Biological Environmental Science and Engineering (BESE) Division, King Abdullah University of Science and Technology (KAUST), Jeddah, Saudi Arabia
| | - Kholoud Ashour
- Dept. of Biochemistry, McGill University, 3655 Promenade Sir William Osler, Montreal, QC H3G1Y6, Canada,Rosalind & Morris Goodman Cancer Institute, McGill University, 1160 Pine Avenue, Montreal, QC H3A1A3, Canada
| | - Jason Sadek
- Dept. of Biochemistry, McGill University, 3655 Promenade Sir William Osler, Montreal, QC H3G1Y6, Canada,Rosalind & Morris Goodman Cancer Institute, McGill University, 1160 Pine Avenue, Montreal, QC H3A1A3, Canada
| | - Xian Jin Lian
- Dept. of Biochemistry, McGill University, 3655 Promenade Sir William Osler, Montreal, QC H3G1Y6, Canada,Rosalind & Morris Goodman Cancer Institute, McGill University, 1160 Pine Avenue, Montreal, QC H3A1A3, Canada
| | - Shahryar Khattak
- KAUST Smart-Health Initiative King Abdullah University of Science and Technology (KAUST), Jeddah, Saudi Arabia,KAUST Biological Environmental Science and Engineering (BESE) Division, King Abdullah University of Science and Technology (KAUST), Jeddah, Saudi Arabia
| | - Sergio Di Marco
- KAUST Smart-Health Initiative King Abdullah University of Science and Technology (KAUST), Jeddah, Saudi Arabia,KAUST Biological Environmental Science and Engineering (BESE) Division, King Abdullah University of Science and Technology (KAUST), Jeddah, Saudi Arabia,Dept. of Biochemistry, McGill University, 3655 Promenade Sir William Osler, Montreal, QC H3G1Y6, Canada,Rosalind & Morris Goodman Cancer Institute, McGill University, 1160 Pine Avenue, Montreal, QC H3A1A3, Canada
| | | |
Collapse
|
46
|
Jung Y, Geng C, Bonvin AMJJ, Xue LC, Honavar VG. MetaScore: A Novel Machine-Learning-Based Approach to Improve Traditional Scoring Functions for Scoring Protein-Protein Docking Conformations. Biomolecules 2023; 13:121. [PMID: 36671507 PMCID: PMC9855734 DOI: 10.3390/biom13010121] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 12/22/2022] [Accepted: 12/26/2022] [Indexed: 01/11/2023] Open
Abstract
Protein-protein interactions play a ubiquitous role in biological function. Knowledge of the three-dimensional (3D) structures of the complexes they form is essential for understanding the structural basis of those interactions and how they orchestrate key cellular processes. Computational docking has become an indispensable alternative to the expensive and time-consuming experimental approaches for determining the 3D structures of protein complexes. Despite recent progress, identifying near-native models from a large set of conformations sampled by docking-the so-called scoring problem-still has considerable room for improvement. We present MetaScore, a new machine-learning-based approach to improve the scoring of docked conformations. MetaScore utilizes a random forest (RF) classifier trained to distinguish near-native from non-native conformations using their protein-protein interfacial features. The features include physicochemical properties, energy terms, interaction-propensity-based features, geometric properties, interface topology features, evolutionary conservation, and also scores produced by traditional scoring functions (SFs). MetaScore scores docked conformations by simply averaging the score produced by the RF classifier with that produced by any traditional SF. We demonstrate that (i) MetaScore consistently outperforms each of the nine traditional SFs included in this work in terms of success rate and hit rate evaluated over conformations ranked among the top 10; (ii) an ensemble method, MetaScore-Ensemble, that combines 10 variants of MetaScore obtained by combining the RF score with each of the traditional SFs outperforms each of the MetaScore variants. We conclude that the performance of traditional SFs can be improved upon by using machine learning to judiciously leverage protein-protein interfacial features and by using ensemble methods to combine multiple scoring functions.
Collapse
Affiliation(s)
- Yong Jung
- Bioinformatics & Genomics Graduate Program, Pennsylvania State University, University Park, PA 16802, USA
- Artificial Intelligence Research Laboratory, Pennsylvania State University, University Park, PA 16802, USA
- Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA 16802, USA
| | - Cunliang Geng
- Bijvoet Centre for Biomolecular Research, Faculty of Science—Chemistry, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
| | - Alexandre M. J. J. Bonvin
- Bijvoet Centre for Biomolecular Research, Faculty of Science—Chemistry, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
| | - Li C. Xue
- Bijvoet Centre for Biomolecular Research, Faculty of Science—Chemistry, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
- Center for Molecular and Biomolecular Informatics, Radboudumc, Greet Grooteplein 26-28, 6525 GA Nijmegen, The Netherlands
| | - Vasant G. Honavar
- Bioinformatics & Genomics Graduate Program, Pennsylvania State University, University Park, PA 16802, USA
- Artificial Intelligence Research Laboratory, Pennsylvania State University, University Park, PA 16802, USA
- Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA 16802, USA
- Clinical and Translational Sciences Institute, Pennsylvania State University, University Park, PA 16802, USA
- College of Information Sciences & Technology, Pennsylvania State University, University Park, PA 16802, USA
- Institute for Computational and Data Sciences, Pennsylvania State University, University Park, PA 16802, USA
- Center for Big Data Analytics and Discovery Informatics, Pennsylvania State University, University Park, PA 16823, USA
| |
Collapse
|
47
|
Guedeney N, Cornu M, Schwalen F, Kieffer C, Voisin-Chiret AS. PROTAC technology: A new drug design for chemical biology with many challenges in drug discovery. Drug Discov Today 2023; 28:103395. [PMID: 36228895 DOI: 10.1016/j.drudis.2022.103395] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 09/06/2022] [Accepted: 10/05/2022] [Indexed: 11/06/2022]
Abstract
Target Protein Degradation TPD is a new avenue and revolutionary for therapeutics because redefining the principles of classical drug discovery and guided by event-based target activity rather than the occupancy-driven activity. Since the discovery of the first PROTAC in 2001, TPD represents a rapidly growing technology, with applications in both drug discovery and chemical biology. Over the last decade, many questions have been raised and today the knowledge gained by each team has elucidated a number of them, although there is still a long way to go. The objective of this work is to present the challenges that the PROTAC strategy has very recently addressed in drug design and discovery by presenting extremely recent results from the literature and to provide guidelines in the drug design of new PROTACs as successful therapeutic modality for medicinal chemists.
Collapse
Affiliation(s)
| | - Marie Cornu
- Normandie Univ, UNICAEN, CERMN, 14000 Caen, France
| | - Florian Schwalen
- Normandie Univ, UNICAEN, CERMN, 14000 Caen, France; Department of Pharmacy, Caen University Hospital, Caen 14000, France
| | | | | |
Collapse
|
48
|
Harini K, Christoffer C, Gromiha MM, Kihara D. Pairwise and Multi-chain Protein Docking Enhanced Using LZerD Web Server. Methods Mol Biol 2023; 2690:355-373. [PMID: 37450159 PMCID: PMC10561630 DOI: 10.1007/978-1-0716-3327-4_28] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
Interactions of proteins with other macromolecules have important structural and functional roles in the basic processes of living cells. To understand and elucidate the mechanisms of interactions, it is important to know the 3D structures of the complexes. Proteomes contain numerous protein-protein complexes, for which experimentally determined structures often do not exist. Computational techniques can be a practical alternative to obtain useful complex structure models. Here, we present a web server that provides access to the LZerD and Multi-LZerD protein docking tools, which can perform both pairwise and multi-chain docking. The web server is user-friendly, with options to visualize the distribution and structures of binding poses of top-scoring models. The LZerD web server is available at https://lzerd.kiharalab.org . This chapter dictates the algorithm and step-by-step procedure to model the monomeric structures with AttentiveDist, and also provides the detail of pairwise LZerD docking, and multi-LZerD. This also provided case studies for each of the three modules.
Collapse
Affiliation(s)
- Kannan Harini
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | | | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
- Department of Computer Science, Purdue University, West Lafayette, IN, USA.
| |
Collapse
|
49
|
Abeywickrama HLTC, Rabindrakumar MSK, Pathira Kankanamge LS, Thoradeniya T, Galhena GH. TMPRSS6 rs855791 polymorphism is associated with iron deficiency in a cohort of Sri Lankan pregnant women. EGYPTIAN JOURNAL OF MEDICAL HUMAN GENETICS 2022. [DOI: 10.1186/s43042-022-00377-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Abstract
Background
Hepcidin is the key regulator of systemic iron homeostasis and is downregulated by matriptase 2 (MT2), a protease encoded by TMPRSS6 gene. In the presence of low iron levels, MT2 cleaves membrane-bound hemojuvelin (HJV), causing a negative regulation of hepcidin at the gene level, and restores iron balance. rs855791T > C, a missense variant in the catalytic domain of MT2, causes valine to alanine change at 736 position. The current study aimed to investigate the association of TMPRSS6 rs855791 on iron status among a cohort of pregnant women in Sri Lanka and to predict the possible molecular mechanisms.
Methods
The study was conducted among 73 pregnant women at ≤ 12 weeks of gestation. Iron deficiency was defined as serum ferritin < 30 μg/L after adjusting for inflammation. rs855791 was genotyped with a PCR–RFLP, and its association with iron deficiency was analyzed using binary logistic regression. Docking of HJV with MT2 protein encoded by the two rs855791 alleles was undertaken in silico to predict the molecular mechanism of the observed associations.
Results
The majority of the study population (70%) were iron deficient. Among the subjects, T allele was prevalent in the iron deficient group with a frequency of 61.8%, with a nearly twofold enhanced risk for iron deficiency (OR = 2.566, 95%CI; P = 0.011). For TT genotype, the risk of iron deficiency was nearly sixfold (OR = 5.867; 95%CI; P = 0.023). According to the in silico analysis, MT2 736A and HJV complex is more stable with an interface energy of − 7.934 kJ/mol compared to the MT2 736 V and HJV complex which generates an interface energy of − 4.689 kJ/mol.
Conclusion
The current study suggests that the iron regulatory effect of rs855791 of TMPRSS6 is brought about by the differences in thermodynamic stability of the two protein complexes made by MT2 and HJV proteins. The prevalence of iron deficiency observed among Sri Lankan pregnant women may be an interplay between the prevalence of rs855791 T allele and the low dietary iron intake.
Collapse
|
50
|
Verma AK, Ahmed SF, Hossain MS, Bhojiya AA, Mathur A, Upadhyay SK, Srivastava AK, Vishvakarma NK, Barik M, Rahaman MM, Bahadur NM. Molecular docking and simulation studies of flavonoid compounds against PBP-2a of methicillin-resistant Staphylococcus aureus. J Biomol Struct Dyn 2022; 40:10561-10577. [PMID: 34243699 DOI: 10.1080/07391102.2021.1944911] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Methicillin-Resistant Staphylococcus aureus (MRSA), a pathogenic bacterium that causes life-threatening outbreaks such as community-onset and nosocomial infections as emerging 'superbug'. Time and motion study of its virulent property developed resistance against most of the antibiotics such as Vancomycin. Thereby, to curb this problem entails the development of new therapeutic agents. Plant-derived antimicrobial agents have recently piqued people's interest, so in this research, 186 flavonoids compound selected to unmask the best candidates that can act as potent inhibitors against the Penicillin Binding Protein-2a (PBP-2a) of MRSA. Molecular docking performed using PyRx and GOLD suite to determine the binding affinities and interactions between the phytochemicals and the PBP-2a. The selected candidates strongly interact with the different amino acid residues. The 30 ns molecular dynamics (MD) simulations with five top-ranked compounds such as Naringin, Hesperidin, Neohesperidin, Didymin and Icariin validated the docking interactions. These findings are also strongly supported by root-mean-square deviation, root-mean-square fluctuation and the radius of gyration. ADME/T analysis demonstrates that these candidates appear to be safer inhibitors. Our findings point to natural flavonoids as a promising and readily available source of adjuvant antimicrobial therapy against resistant strains in the future.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Abhishek Kumar Verma
- Department of Life Sciences, Faculty of Science and Technology, Mewar University, Gangrar, Rajasthan, India
| | - Sk Faisal Ahmed
- Department of Biotechnology and Genetic Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh
| | - Md Shahadat Hossain
- Department of Biotechnology and Genetic Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh
| | - Ali Asger Bhojiya
- Faculty of Agriculture and Veterinary Sciences, Mewar University, Gangrar, Rajasthan, India
| | - Ankita Mathur
- Department of Life Sciences, Faculty of Science and Technology, Mewar University, Gangrar, Rajasthan, India
| | - Sudhir K Upadhyay
- Department of Environmental Sciences, V.B.S. Purvanchal University, Jaunpur, Uttar Pradesh, India
| | | | | | - Mayadhar Barik
- Department of Life Sciences, Faculty of Science and Technology, Mewar University, Gangrar, Rajasthan, India.,Research Section, Mewar University, Gangrar, Rajasthan, India
| | | | - Newaz Mohammed Bahadur
- Department of Applied Chemistry and Chemical Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh
| |
Collapse
|